{"text_format":"1","workbook":"xlsx","n_sheets":81,"sheets":["Topics","Detailed Topic List","Keyboards","Notes","Location","Location (an)","Terms","MeanPorS","MeanPorS (an)","Mean","Mean (an)","Median","Median (an)","Mode","Mode (an)","Skew","Skew (an)","Weighted Mean","Weighted Mean (an)","WM(2)","WM(2an)","GroupedDataMean","Trimmed Mean","Geometric Mean","Geometric Mean (an)","PercentiileAndQuartile","PercentiileAndQuartile (an)","Rank","Stock","Rank (an)","V(1)","V(2)","V(3)","V(4)","V(4an)","V(5)","V(5an)","V(6)","V(7)","V(7an)","V(8)","V(9)","V(9an)","V(10)","DS","DS (an)","Skew (2)","Skew (2an)","Skew (3)","S(L)","S(B)","S(R)","S(HR)","z(1)","z(1an)","z(2)","z(2an)","z(3)","z(3an)","Chebyshev","Chebyshev (an)","Empirical Rule","HeightChart","Empirical Rule (an)","Bell&Cheb","Bell&Cheb (an)","5 N S","5 N S (an)","5 Number & Box Blot","5 Number & Box Blot (an)","Outliers","Outliers (an)","BP Chart","Multiple BP","Monthly Salary","2 Variable Notes","2 Variable Scatter","2 Variable Xbar Lines","2 Variable Xbar Lines (an)","2 Variables Cov and Corr","2 Variables Cov and Corr (an)"],"not_rendered":{"charts":90,"images":21,"macros":0,"external_links":0,"defined_names":11,"comments":0,"pivot_tables":2},"unopened":[],"bounds":{"document_bytes":16777216,"sheet_cells":100000,"rows_first":2000,"rows_last":500,"rows_between":2000,"halvings":0,"cell_chars":16384,"cell_head_chars":4096},"cells_cut":0,"cells_raw":0,"read":"whole","date_system":"1900"}

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["Descriptive Statistics: Tabular, Graphical and Numerical summaries of data."]
[]
["Chapter 2: Descriptive Statistics: Tabular and Graphical summaries of data."]
[]
["Chapter 3: Descriptive Statistics: Numerical Measures of data."]
[]
["Topics","Sheet Name (link to jump)"]
["Notes",{"$f":"A8","v":"Notes"}]
["Terms",{"$f":"A9","v":"Terms"}]
["Measures of Location:"]
["Average = Typical Value"]
["Mean","MeanPorS"]
["Median",{"$f":"A13","v":"Median"}]
["Mode","Mode"]
["Skew and Mean, Median, Mode","Skew"]
["Weighted Mean",{"$f":"A16","v":"Weighted Mean"}]
["Geometric Mean",{"$f":"A17","v":"Geometric Mean"}]
["Percentiles & Quartiles","PercentiileAndQuartile"]
["Percentile Rank","Rank"]
["Measures of Variability:"]
["Visual","V(1)"]
["Range and Interquartile Range","V(5)"]
["Variance","V(6)"]
["Standard Deviation","V(7)"]
["Coefficient of Variation","V(9)"]
["Descriptive Statistics Data Analysis Tool","DS"]
["Measures of Distribution of Shape:"]
["Remember Histograms","Skew (2)"]
["Z-scores","z(1)"]
["Chebyshev's Rule","Chebyshev"]
["Empirical Rule","Empirical Rule"]
["Detecting Outliers","Outliers"]
["Five-Number Summaries and Box & Plots","5 N S"]
["Measures of Association:"]
["Covariance and Correlation Coefficient","2 Variable Scatter"]

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["Video16:","Excel 2013 Statistical Analysis #16: Location: Mean, Median, Mode for Central Location"]
["Number","Topic In Video and minute mark in Video.","Time In Video",null,null,null,null,null,null,null,null,null,"Text"]
[{"$f":"ROWS(A3:A$3)","v":1},{"$f":"TRIM(REPLACE(M3,1,SEARCH(\")\",M3)+1,\"\"))","v":"Introduction to averages as typical values that represent a single value that represents all the data points"},{"$f":"TRIM(LEFT(M3,SEARCH(\")\",M3)))","v":"(00:32)"},null,null,null,null,null,null,null,null,null,"(00:32) Introduction to averages as typical values that represent a single value that represents all the data points"]
[{"$f":"ROWS(A$3:A4)","v":2},{"$f":"TRIM(REPLACE(M4,1,SEARCH(\")\",M4)+1,\"\"))","v":"Introduction to Location"},{"$f":"TRIM(LEFT(M4,SEARCH(\")\",M4)))","v":"(01:17)"},null,null,null,null,null,null,null,null,null,"(01:17) Introduction to Location"]
[{"$f":"ROWS(A$3:A5)","v":3},"Sample Statistics are point estimators for the population parameters","(03:55)",null,null,null,null,null,null,null,null,null,"(03:55) Sample Statistics are point estimators for the population parameters"]
[{"$f":"ROWS(A$3:A6)","v":4},"Mean using AVERAGE function","(05:28)",null,null,null,null,null,null,null,null,null,"(05:28) Mean using AVERAGE function"]
[{"$f":"ROWS(A$3:A7)","v":5},"Median using sorting or the MEDIAN function","(09:16)",null,null,null,null,null,null,null,null,null,"(09:16) Median using sorting or the MEDIAN function"]
[{"$f":"ROWS(A$3:A8)","v":6},"Mode for Categorical data using COUNTIFS and MODE for quatitative data using MODE.SNGL. Discussion about Bi-modal and multiple modes and why using a PivotTable rather than MODE.SNGL is wise.","(15:54)",null,null,null,null,null,null,null,null,null,"(15:54) Mode for Categorical data using COUNTIFS and MODE for quatitative data using MODE.SNGL. Discussion about Bi-modal and multiple modes and why using a PivotTable rather than MODE.SNGL is wise."]
[{"$f":"ROWS(A$3:A9)","v":7},"Relationship between Mean, Median and Mode and skew.","(21:42)",null,null,null,null,null,null,null,null,null,"(21:42) Relationship between Mean, Median and Mode and skew."]
[]
["Video17:","Excel 2013 Statistical Analysis #17: Weighted Mean & SUMPRODUCT Function & Accounting Example"]
["Number","Topic In Video and minute mark in Video.","Time In Video",null,null,null,null,null,null,null,null,null,"Text"]
[{"$f":"ROWS(A$13:A13)","v":1},{"$f":"TRIM(REPLACE(M13,1,SEARCH(\")\",M13)+1,\"\"))","v":"Weighted Mean Quiz Score Example and Weighted Mean calculation using a Helper Column"},{"$f":"TRIM(LEFT(M13,SEARCH(\")\",M13)))","v":"(00:14)"},null,null,null,null,null,null,null,null,null,"(00:14) Weighted Mean Quiz Score Example and Weighted Mean calculation using a Helper Column"]
[{"$f":"ROWS(A$3:A14)","v":12},{"$f":"TRIM(REPLACE(M14,1,SEARCH(\")\",M14)+1,\"\"))","v":"SUMPRODUCT and SUM function for single Cell Formula"},{"$f":"TRIM(LEFT(M14,SEARCH(\")\",M14)))","v":"(02:47)"},null,null,null,null,null,null,null,null,null,"(02:47) SUMPRODUCT and SUM function for single Cell Formula"]
[{"$f":"ROWS(A$3:A15)","v":13},"Calculate Weighted Average for Accounting COGS and End Inventory","(04:47)",null,null,null,null,null,null,null,null,null,"(04:47) Calculate Weighted Average for Accounting COGS and End Inventory"]
[{"$f":"ROWS(A$3:A16)","v":14},"alculate Weighted Average or “Expected Value” given a Percent Frequency Table","(08:10)",null,null,null,null,null,null,null,null,null,"(08:10)Calculate Weighted Average or “Expected Value”  given a Percent Frequency Table"]
[]
["Video18:","Excel 2013 Statistical Analysis #18: Geometric Mean, Average Compounding Rate GEOMEAN & RRI"]
["Number","Topic In Video and minute mark in Video.","Time In Video",null,null,null,null,null,null,null,null,null,"Text"]
[{"$f":"ROWS(A$20:A20)","v":1},{"$f":"TRIM(REPLACE(M20,1,SEARCH(\")\",M20)+1,\"\"))","v":"Intro to Geometric Mean or Average Compounding Rate"},{"$f":"TRIM(LEFT(M20,SEARCH(\")\",M20)))","v":"(00:09)"},null,null,null,null,null,null,null,null,null,"(00:09) Intro to Geometric Mean or Average Compounding Rate"]
[{"$f":"ROWS(A$20:A21)","v":2},{"$f":"TRIM(REPLACE(M21,1,SEARCH(\")\",M21)+1,\"\"))","v":"Growth Rate or Percentage Change Formulas and Growth Factor"},{"$f":"TRIM(LEFT(M21,SEARCH(\")\",M21)))","v":"(00:52)"},null,null,null,null,null,null,null,null,null,"(00:52) Growth Rate or Percentage Change Formulas and Growth Factor"]
[{"$f":"ROWS(A$20:A22)","v":3},"Why you should not use the Mean when dealing with Growth Rates","(02:36)",null,null,null,null,null,null,null,null,null,"(02:36) Why you should not use the Mean when dealing with Growth Rates"]
[{"$f":"ROWS(A$20:A23)","v":4},"FV formula","(03:15)",null,null,null,null,null,null,null,null,null,"(03:15) FV formula"]
[{"$f":"ROWS(A$20:A24)","v":5},"eometric Mean or Average Compounding Rate using GEOMEAN function when you are given a list of growth rates or Percentage Changes. Stock Price Example","(04:48)",null,null,null,null,null,null,null,null,null,"(04:48)Geometric Mean or Average Compounding Rate using GEOMEAN function when you are given a list of growth rates or Percentage Changes. Stock Price Example"]
[{"$f":"ROWS(A$20:A25)","v":6},"Geometric Mean or Average Compounding Rate using Formula 2 and RRI function when you are given a begin amount (PV) and an end amount (FV)) and the number of periods. Stock Price Example","(06:25)",null,null,null,null,null,null,null,null,null,"(06:25) Geometric Mean or Average Compounding Rate using Formula 2 and RRI function when you are given a begin amount (PV) and an end amount (FV)) and the number of periods. Stock Price Example"]
[{"$f":"ROWS(A$20:A26)","v":7},"Investment example","(08:08)",null,null,null,null,null,null,null,null,null,"(08:08) Investment example"]
[{"$f":"ROWS(A$20:A27)","v":8},"Population Change example","(09:00)",null,null,null,null,null,null,null,null,null,"(09:00) Population Change example"]
[]
["Video19:","Excel 2013 Statistical Analysis #19: Location: Percentile, Quartile, Rank and Percentile Rank"]
["Number","Topic In Video and minute mark in Video.","Time In Video",null,null,null,null,null,null,null,null,null,"Text"]
[{"$f":"ROWS(A$31:A31)","v":1},{"$f":"TRIM(REPLACE(M31,1,SEARCH(\")\",M31)+1,\"\"))","v":"Percentile where position is calculated with P%*(n+1)"},{"$f":"TRIM(LEFT(M31,SEARCH(\")\",M31)))","v":"(00:24)"},null,null,null,null,null,null,null,null,null,"(00:24) Percentile where position is calculated with P%*(n+1)"]
[{"$f":"ROWS(A$31:A32)","v":2},{"$f":"TRIM(REPLACE(M32,1,SEARCH(\")\",M32)+1,\"\"))","v":"PERCENTILE.EXC function"},{"$f":"TRIM(LEFT(M32,SEARCH(\")\",M32)))","v":"(04:26)"},null,null,null,null,null,null,null,null,null,"(04:26) PERCENTILE.EXC function"]
[{"$f":"ROWS(A$31:A33)","v":3},"What percentile tells you","(05:18)",null,null,null,null,null,null,null,null,null,"(05:18) What percentile tells you"]
[{"$f":"ROWS(A$31:A34)","v":4},"QUARTILE.EXC function","(06:09)",null,null,null,null,null,null,null,null,null,"(06:09) QUARTILE.EXC function"]
[{"$f":"ROWS(A$31:A35)","v":5},"PERCENTILERANK.EXC function","(06:59)",null,null,null,null,null,null,null,null,null,"(06:59) PERCENTILERANK.EXC function"]
[{"$f":"ROWS(A$31:A36)","v":6},"Median","(07:45)",null,null,null,null,null,null,null,null,null,"(07:45) Median"]
[{"$f":"ROWS(A$31:A37)","v":7},"Quartile 1, 2, 3","(08:48)",null,null,null,null,null,null,null,null,null,"(08:48) Quartile 1, 2, 3"]
[{"$f":"ROWS(A$31:A38)","v":8},"Quintiles","(09:34)",null,null,null,null,null,null,null,null,null,"(09:34) Quintiles"]
[{"$f":"ROWS(A$31:A39)","v":9},"Deciles","(10:03)",null,null,null,null,null,null,null,null,null,"(10:03) Deciles"]
[{"$f":"ROWS(A$31:A40)","v":10},"Percentile rank for whole data set","(10:24)",null,null,null,null,null,null,null,null,null,"(10:24) Percentile rank for whole data set"]
[{"$f":"ROWS(A$31:A41)","v":11},"PERCENTILE.INC, QUARTILE.INC functions","(11:24)",null,null,null,null,null,null,null,null,null,"(11:24) PERCENTILE.INC, QUARTILE.INC functions"]
[{"$f":"ROWS(A$31:A42)","v":12},"Five Number Summary","(14:13)",null,null,null,null,null,null,null,null,null,"(14:13) Five Number Summary"]
[{"$f":"ROWS(A$31:A43)","v":13},"Big Data Set to see that PERCENTILE.INC and PERCENTILE.EXC are very close if not exactly the same.","(14:40)",null,null,null,null,null,null,null,null,null,"(14:40) Big Data Set to see that PERCENTILE.INC and PERCENTILE.EXC are very close if not exactly the same."]
[{"$f":"ROWS(A$31:A44)","v":14},"Ranking. RANK,EQ and RANK.AVE functions","(16:29)",null,null,null,null,null,null,null,null,null,"(16:29) Ranking. RANK,EQ and RANK.AVE functions"]
[{"$f":"ROWS(A$31:A45)","v":15},"PERCENTILERANK.INC function","(18:46)",null,null,null,null,null,null,null,null,null,"(18:46) PERCENTILERANK.INC function"]
[{"$f":"ROWS(A$31:A46)","v":16},"Data Ribbon Tab, Analysis group, Data Analysis, Rank and Percentile feature","(19:00)",null,null,null,null,null,null,null,null,null,"(19:00) Data Ribbon Tab, Analysis group, Data Analysis, Rank and Percentile feature"]
[]
["Video20:","Excel 2013 Statistical Analysis #20: Standard Deviation: How Fairly Does Mean Represent Data Points?"]
["Number","Topic In Video and minute mark in Video.","Time In Video",null,null,null,null,null,null,null,null,null,"Text"]
[{"$f":"ROWS(A$50:A50)","v":1},{"$f":"TRIM(REPLACE(M50,1,SEARCH(\")\",M50)+1,\"\"))","v":"Three data sets and charts of each to illustrate the concept of Variability, or Dispersion, or Spread in the Data."},{"$f":"TRIM(LEFT(M50,SEARCH(\")\",M50)))","v":"(00:09)"},null,null,null,null,null,null,null,null,null,"(00:09) Three data sets and charts of each to illustrate the concept of Variability, or Dispersion, or Spread in the Data."]
[{"$f":"ROWS(A$50:A51)","v":2},{"$f":"TRIM(REPLACE(M51,1,SEARCH(\")\",M51)+1,\"\"))","v":"Set 1 (Hours Worked per Week Data), we ask the question: How fairly does the mean represent its data points?"},{"$f":"TRIM(LEFT(M51,SEARCH(\")\",M51)))","v":"(00:20)"},null,null,null,null,null,null,null,null,null,"(00:20) Set 1 (Hours Worked per Week Data), we ask the question: How fairly does the mean represent its data points?"]
[{"$f":"ROWS(A$50:A52)","v":3},"Set 2 (Seattle and Burien Gas Prices), we ask the question: How volatile are Prices?","(02:37)",null,null,null,null,null,null,null,null,null,"(02:37) Set 2 (Seattle and Burien Gas Prices), we ask the question: How volatile are Prices?"]
[{"$f":"ROWS(A$50:A53)","v":4},"Set 3 (Supplier Days To Fill Order Data), we ask the question: How reliable is a Supplier?","(03:17)",null,null,null,null,null,null,null,null,null,"(03:17) Set 3 (Supplier Days To Fill Order Data), we ask the question: How reliable is a Supplier?"]
[{"$f":"ROWS(A$50:A54)","v":5},"Calculate Range: Max - Min","(04:37)",null,null,null,null,null,null,null,null,null,"(04:37) Calculate Range: Max - Min"]
[{"$f":"ROWS(A$50:A55)","v":6},"Calculate Interquartile range: Quartile 3 – Quartile 1 = Middle 50% of the values","(05:50)",null,null,null,null,null,null,null,null,null,"(05:50) Calculate Interquartile range: Quartile 3 – Quartile 1 = Middle 50% of the values"]
[{"$f":"ROWS(A$50:A56)","v":7},"Variance and Standard Deviation Math Formulas for populations and samples.","(07:17)",null,null,null,null,null,null,null,null,null,"(07:17) Variance and Standard Deviation Math Formulas for populations and samples."]
[{"$f":"ROWS(A$50:A57)","v":8},"Calculate Sample Standard Deviation Long Hand and with STDEV.S for Data Set 3 (Supplier Days To Fill Order Data), were we ask the question: How reliable is a Supplier?","(08:13)",null,null,null,null,null,null,null,null,null,"(08:13) Calculate Sample Standard Deviation Long Hand and with STDEV.S for Data Set 3 (Supplier Days To Fill Order Data), were we ask the question: How reliable is a Supplier?"]
[{"$f":"ROWS(A$50:A58)","v":9},"Alternative Formula for Sample Standard Deviation","(16:45)",null,null,null,null,null,null,null,null,null,"(16:45) Alternative Formula for Sample Standard Deviation"]
[{"$f":"ROWS(A$50:A59)","v":10},"Mean Absolute Error Formula","(17:05)",null,null,null,null,null,null,null,null,null,"(17:05) Mean Absolute Error Formula"]
[{"$f":"ROWS(A$50:A60)","v":11},"Calculate Sample Standard Deviation with STDEV.S for Data Set 1, (Hours Worked per Week Data) were we ask the question: How fairly does the mean represent its data points?","(17:20)",null,null,null,null,null,null,null,null,null,"(17:20) Calculate Sample Standard Deviation with STDEV.S for Data Set 1, (Hours Worked per Week Data) were we ask the question: How fairly does the mean represent its data points?"]
[{"$f":"ROWS(A$50:A61)","v":12},"Calculate Sample Standard Deviation with STDEV.S for Set 2 (Seattle and Burien Gas Prices), were we ask the question: How volatile are Prices?","(18:06)",null,null,null,null,null,null,null,null,null,"(18:06) Calculate Sample Standard Deviation with STDEV.S for Set 2 (Seattle and Burien Gas Prices), were we ask the question: How volatile are Prices?"]
[{"$f":"ROWS(A$50:A62)","v":13},"Coefficient of Variation","(19:25)",null,null,null,null,null,null,null,null,null,"(19:25) Coefficient of Variation"]
[{"$f":"ROWS(A$50:A63)","v":14},"Using Descriptive Statistics feature to quickly calculate Numerical Measures like Mean, Median, Mode, Standard Deviation and more","(23:25)",null,null,null,null,null,null,null,null,null,"(23:25) Using Descriptive Statistics feature to quickly calculate Numerical Measures like Mean, Median, Mode, Standard Deviation and more"]
[{"$f":"ROWS(A$50:A64)","v":15},"X-Y Scatter Chart to Visually Portray Variation or Dispersion or Spread in the Data.","(24:52)",null,null,null,null,null,null,null,null,null,"(24:52) X-Y Scatter Chart to Visually Portray Variation or Dispersion or Spread in the Data."]
[{"$f":"ROWS(A$50:A65)","v":16},"Summary of topics in video","(25:09)",null,null,null,null,null,null,null,null,null,"(25:09) Summary of topics in video"]
[]
["Video21:","Excel 2013 Statistical Analysis #21: SKEW Function and Asymmetry in a Distribution of Data Points"]
["Number","Topic In Video and minute mark in Video.","Time In Video",null,null,null,null,null,null,null,null,null,"Text"]
[{"$f":"ROWS(A$69:A69)","v":1},"This video covers skew in a distribution of data points and the SKEW function.","(00:08)",null,null,null,null,null,null,null,null,null,"This video covers skew in a distribution of data points and the SKEW function."]
[]
["Video22:","Excel 2013 Statistical Analysis #22: Z-Score = # of Standard Deviations, Chebshev’s &Empirical Rule"]
["Number","Topic In Video and minute mark in Video.","Time In Video",null,null,null,null,null,null,null,null,null,"Text"]
[{"$f":"ROWS(A$73:A73)","v":1},{"$f":"TRIM(REPLACE(M73,1,SEARCH(\")\",M73)+1,\"\"))","v":"Calculate z score. Z Score is the Number of Standard Deviations a particular value is away from the mean. Deviation divided by Standard Deviation formula or STANDARDIZE function."},{"$f":"TRIM(LEFT(M73,SEARCH(\")\",M73)))","v":"(00:09)"},null,null,null,null,null,null,null,null,null,"(00:09) Calculate z score. Z Score is the Number of Standard Deviations a particular value is away from the mean. Deviation divided by Standard Deviation formula or STANDARDIZE function."]
[{"$f":"ROWS(A$73:A74)","v":2},{"$f":"TRIM(REPLACE(M74,1,SEARCH(\")\",M74)+1,\"\"))","v":"Chebshev’s Rule: 5 Examples"},{"$f":"TRIM(LEFT(M74,SEARCH(\")\",M74)))","v":"(08:15)"},null,null,null,null,null,null,null,null,null,"(08:15) Chebshev’s Rule: 5 Examples"]
[{"$f":"ROWS(A$73:A75)","v":3},"Empirical Rule: 12 Examples","(14:03)",null,null,null,null,null,null,null,null,null,"(14:03) Empirical Rule: 12 Examples"]
[]
["Video23:","Excel 2013 Statistical Analysis #22: Z-Score = # of Standard Deviations, Chebshev’s &Empirical Rule"]
["Number","Topic In Video and minute mark in Video.","Time In Video",null,null,null,null,null,null,null,null,null,"Text"]
[{"$f":"ROWS(A$79:A79)","v":1},{"$f":"TRIM(REPLACE(M79,1,SEARCH(\")\",M79)+1,\"\"))","v":"Five Number Summary: Min, Quartile 1, Quartile 2, Quartile 3 and Max"},{"$f":"TRIM(LEFT(M79,SEARCH(\")\",M79)))","v":"(00:09)"},null,null,null,null,null,null,null,null,null,"(00:09) Five Number Summary: Min, Quartile 1, Quartile 2, Quartile 3 and Max"]
[{"$f":"ROWS(A$79:A80)","v":2},{"$f":"TRIM(REPLACE(M80,1,SEARCH(\")\",M80)+1,\"\"))","v":"Box Plots: Visual Summary of the Five Number Summary. How to calculate numbers with Excel and draw chart on paper"},{"$f":"TRIM(LEFT(M80,SEARCH(\")\",M80)))","v":"(02:41)"},null,null,null,null,null,null,null,null,null,"(02:41) Box Plots: Visual Summary of the Five Number Summary. How to calculate numbers with Excel and draw chart on paper"]
[{"$f":"ROWS(A$79:A81)","v":3},"+/- 3 Standard Deviation Outliers","(07:55)",null,null,null,null,null,null,null,null,null,"(07:55) +/- 3 Standard Deviation Outliers"]
[{"$f":"ROWS(A$79:A82)","v":4},"ABS function to find outliers","(08:37)",null,null,null,null,null,null,null,null,null,"(08:37) ABS function to find outliers"]
[{"$f":"ROWS(A$79:A83)","v":5},"+/- 3 Standard Deviation Outliers, COUNTIFS to count TRUE values","(09:17)",null,null,null,null,null,null,null,null,null,"(09:17) +/- 3 Standard Deviation Outliers, COUNTIFS to count TRUE values"]
[{"$f":"ROWS(A$79:A84)","v":6},"+/- 3 Standard Deviation Outliers, Conditional Formatting to highlight Outliers","(10:08)",null,null,null,null,null,null,null,null,null,"(10:08) +/- 3 Standard Deviation Outliers, Conditional Formatting to highlight Outliers"]
[]
["Video24:","Excel 2013 Statistical Analysis #24: Numerical Measures: Covariance and Correlation Coefficient"]
["Number","Topic In Video and minute mark in Video.","Time In Video",null,null,null,null,null,null,null,null,null,"Text"]
[{"$f":"ROWS(A$88:A88)","v":1},{"$f":"TRIM(REPLACE(M88,1,SEARCH(\")\",M88)+1,\"\"))","v":"Review of different X-Y Scatter"},{"$f":"TRIM(LEFT(M88,SEARCH(\")\",M88)))","v":"(00:15)"},null,null,null,null,null,null,null,null,null,"(00:15) Review of different X-Y Scatter"]
[{"$f":"ROWS(A$88:A89)","v":2},{"$f":"TRIM(REPLACE(M89,1,SEARCH(\")\",M89)+1,\"\"))","v":"Add Xbar Line and Ybar Line to X Y Scatter Chart to help interpret how Covariance is calculated and why the math behind the calculation makes sense"},{"$f":"TRIM(LEFT(M89,SEARCH(\")\",M89)))","v":"(02:42)"},null,null,null,null,null,null,null,null,null,"(02:42) Add Xbar Line and Ybar Line to X Y Scatter Chart to help interpret how Covariance is calculated and why the math behind the calculation makes sense"]
[{"$f":"ROWS(A$88:A90)","v":3},"Formulas and Calculations for Covariance and Correlation Coefficient (Long Hand)","(08:36)",null,null,null,null,null,null,null,null,null,"(08:36) Formulas and Calculations for Covariance and Correlation Coefficient (Long Hand)"]
[{"$f":"ROWS(A$88:A91)","v":4},"COVARIANCE.S Excel function for calculation Sample Covariance","(12:38)",null,null,null,null,null,null,null,null,null,"(12:38) COVARIANCE.S Excel function for calculation Sample Covariance"]
[{"$f":"ROWS(A$88:A92)","v":5},"Correlation Coefficient & PEARSON Excel function for calculation Correlation Coefficient","(13:41)",null,null,null,null,null,null,null,null,null,"(13:41) Correlation Coefficient & PEARSON Excel function for calculation Correlation Coefficient"]
[{"$f":"ROWS(A$88:A93)","v":6},"CORREL Excel function for calculation Correlation Coefficient","(16:11)",null,null,null,null,null,null,null,null,null,"(16:11) CORREL Excel function for calculation Correlation Coefficient"]
[{"$f":"ROWS(A$88:A94)","v":7},"Summary","(18:50)",null,null,null,null,null,null,null,null,null,"(18:50) Summary"]

{"sheet":"Keyboards","index":2,"n_rows":35,"n_cols":4,"rows_kept":35,"cut":false,"hidden":false,"merged":[]}
["Primary Key","Action","Keyboard Short Cut","Category"]
[1,"Move the Active Worksheet up or down","Ctrl + PageUp/PageDown","Navigation"]
[2,"Put content in Cell and move selected Cell down","Enter","Entering Data in a Cell"]
[3,"Put content in Cell and keep Cell selected","Ctrl + Enter","Entering Data in a Cell"]
[4,"Undo","Ctrl + Z","Basic"]
[5,"Copy","Ctrl + C","Basic"]
[6,"Paste","Ctrl + V","Basic"]
[7,"Cut","Ctrl + X","Basic"]
[8,"Bold","Ctrl + B","Basic"]
[9,"Put Cell in Edit Mode","F2","Formula Edit Mode Keys"]
[10,"Arrow to point to a formula Cell Reference","Arrow Keys","Formula Edit Mode Keys"]
[11,"SUM function","Alt + =","Formula Edit Mode Keys"]
[12,"Delete Cell content","Delete","Basic"]
[13,"Lock Cell Reference (make Absolute)","F4","Formula Edit Mode Keys"]
[14,"Put content in Cell and move selected Cell to right","Tab","Entering Data in a Cell"]
[15,"Put content in Cell and move selected Cell to left","Shift + Tab","Entering Data in a Cell"]
[16,"Jump to bottom of data set (Current Range)","Ctrl + Arrow","Navigation"]
[17,"Select to bottom of data set (Current Range)","Ctrl + Shift + Enter","Formula Edit Mode Keys"]
[18,"Put content in Cell and move selected Cell up","Shift + Enter","Entering Data in a Cell"]
[19,"Jump Back to Active Cell","Ctrl + Backspace","Formula Edit Mode Keys"]
[20,"Open Create PivotTable  dialog box","Alt, N, V","PivotTable"]
[21,"Format Chart Element with Task Pane","Ctrl + 1","Chart"]
[22,"Open Format Cells dialog box","Ctrl + 1","Format Cells"]
[23,"Jump to Cell A1","Ctrl + Home","Navigation"]
[24,"Jump to last Cell used","Ctrl + End","Navigation"]
[25,"Open Control Panel","Window + Pause/Break","Window Key"]
[26,"Show Desktop","Window + D\nor\nWindow + M","Window Key"]
[27,"Select Whole Table (Select Current Region)","Ctrl + * (on Number Pad)\nor\nCtrl + Shift + 8","Selection"]
[28,"Select Ranges not next to each other (noncontiguous ranges)","Ctrl + Mouse Click","Selection"]
[29,"Open Print Options and Print Preview view","Ctrl + P","Basic"]
[30,"Close Print Options and Print Preview view ","Esc","Basic"]
[31,"New Workbook File","Ctrl + N","Basic"]
[32,"Save As Dialog Box","F12","Basic"]
[33,"Find and Replace","Ctrl + Home","Basic"]
[34]

{"sheet":"Notes","index":3,"n_rows":197,"n_cols":4,"rows_kept":197,"cut":false,"hidden":false,"merged":[]}
["Measures of Location:"]
["Average = Typical Value = Measure of central location"]
["\"Typical Values\" calculated so that we have one value that can represent all the data points."]
["Mean"]
["Arithmetic Mean: Add them up and divide by the count"]
["Good for quantitative data when there are not extreme values - extreme values can make the mean look too big or too small (Median more representative of a typical value in that case)"]
["Use AVERAGE function"]
["Median"]
["Sort, then take the one in the middle. If count odd, take one in middle, if even, average middle two."]
["Marks the point in the sorted list (an actual number) where 50% of the numbers are above and 50% of the numbers are below"]
["Good for quantitative data when there are extreme values (like house prices and salaries)"]
["Use MEDIAN function"]
["Mode"]
["One that occurs most frequently (can be bimodal, multimodal)"]
["Good for Categorical Data (Nominal and Ordinal)"]
["Use MODE.SNGL for quantitative data and COUNTIF or PivotTable for Categorical or quantitative data. MODE.SNGL will only show 1 mode if the data set is bi-modal or multi-modal. MODE.MULT can be used for multiple modes."]
["Weighted Mean"]
["Fast way to calculate Mean when you have a Frequency Table or Relative Frequency Table"]
["Use SUMPRODUCT(Values, Weights)/SUM(Weights)"]
["Geometric Mean"]
["Often used when you have \"Growth Rates\" or \"Rates of Change\"",null,null,"\"Rates of Change\" and \"Growth Rates\" are synonyms"]
["Growth Rates or Rate of Change = % change from one period to the next"]
["Growth Factor = Growth Rate + 1 = Factor you can multiply by Begin Value to get End Value. Growth Factor ALWAYS >= 0"]
["MUST use Geometric Mean (not arithmetic mean) if you want the True \"Average\" Compounding Rate per Period"]
["Arithmetic Mean overestimates"]
["Arithmetic Mean is for additive processes; Geomean is for multiplicative processes"]
["In finance, when calculating End Values, use Geometric Mean; however, arithmetic mean is used in some situations like for Standard Deviation, Correlation, and other calculations that do not require True \"Average\" Compounding Rate per Period."]
["Geometric Mean Formula 1:"]
["Use when you are given all the Growth Rates"]
["Written description: nth root of the product of n values (values = growth rates + 1, n = Number of Periods), then subtract 1"]
["In Excel use:"]
["GEOMEAN(RangeOfGrowthRates+1) = Growth Factor"]
["GEOMEAN(RangeOfGrowthRates+1)-1 = Growth Rates or Rate of Change"]
["Geometric Mean Formula 2:"]
["Use when you are given the Begin Value, End Value and the number of periods"]
["(EndValue/BegValue)^(1/NumberOfPeriods)-1"]
["RRI(NumberOfPeriods,BegValue, EndValue)  or  RRI(n,PV,FV)"]
["Percentiles/Quartiles"]
["Marker that divides the sorted data set and says what % are above and what % are below"]
["Percentiles"]
["Provides information about how the data are spread over an interval from smallest to largest value."]
["Marks the point in the ascending sorted list (an actual number) where approximately X% of the numbers are below and approximately 1-X% of the numbers are above the marking point."]
["For quantitative data without a lot of duplicates"]
["Use PERCENTILE.EXC function, which uses formula: P%*(n+1) to get the position"]
["Note: There is NOT just one way to calculate percentiles. Percentile calculations are estimations, this is why we use the word \"approximate\" when we talk about percentiles"]
["Quartiles"]
["Create 3 Value Marking Points to divide data set into 4 parts"]
["Use QUARTILE.EXC function"]
["Percentile Rank"]
["PERCENTRANK.EXC opposite of PERCENTILE.EXC. Whereas PERCENTILE.EXC calculates the marker value when you give it a P%, PERCENTRANK.EXC calculates P% when you give it a value."]
["Rank"]
["Counting, 1st, 2nd, 3rd for \"Biggest to Smallest\" or \"Smallest to Biggest\""]
["Use RANK.AVG or RANK.EQ functions"]
[]
["Variability"]
["Dispersion"]
["Spread In Data"]
["How Spread Out Is Data?"]
["Are the Data Points Clustered Around the Mean?"]
["Does the Mean Fairly Represent the Data Points?"]
["Range"]
["Max - Min"]
["Simple to calculate. Sensitive to extreme values"]
["Interquartile Range"]
["Quartile 3 - Quartile 1"]
["The range for the middle 50% of the data. It overcomes the sensitivity to extreme values"]
["Deviation"]
["How far one datum is from the mean. \"How far above or below the mean is the particular value?\""]
["\"Deviation about the mean\""]
["For any data set, the sum of the deviations is always zero!!!! This is why mathematically, we either square (Variance or Standard Deviation) or take the absolute value (Mean Absolute Value)"]
["Variance"]
["A Numerical Measure that says how much variability there is in the data points"]
["Variance uses all the data points, not just some like Range and Interquartile Range"]
["Variance has squared units, which makes interpreting it difficult. Standard Deviation undoes the squared units and is thus easier to interpret and is more commonly used"]
["Use VAR.P function for population data and VAR.S for sample data."]
["Standard Deviation = SD"]
["Standard Deviation uses all the data points, not just some like Range and Interquartile Range"]
["Standard Deviation does not have squared units (like Variance) and is thus easier to interpret; the standard deviation has the same units as the data!!"]
["Population SD = \"little sigma\" = s ; Sample SD = s"]
["The sample standard deviation is a point estimator of the population standard deviation"]
["Interpretation of Standard Deviation:"]
["A Numerical Measure that says how much variability there is in the data points"]
["Standard Deviation Is Like An Average Of The Deviations"]
["Standard Deviation tells us how fairly the mean represents its data points"]
["Standard Deviation tells us how clustered the data points are around the mean"]
["For financial assets standard deviation is a measure of risk or fluctuation in asset value"]
["Use STDEV.P function for population data and STDEV.S for sample data."]
["Coefficient of Variation"]
["Coefficient of Variation converts the SD to SD per unit of Mean"]
["Formula = SD/Mean"]
["For every one unit of mean, what is the SD?"]
["If you add Percent Number Formatting, it shows SD as a percentage of Mean"]
["What percentage is SD in relation to the Mean?"]
["Use Coefficient Of Variation to compare:"]
["1) Data in different units."]
["     or"]
["2) Data in the same units, but the means are far apart."]
[]
["Distribution, Relative Location, Outliers"]
["Shape of Distribution"]
["Histograms"]
["Histograms show the shape of the distribution visually."]
["A few short column heights to the left means skew negative of left"]
["A few tall column heights to the right means skew positive or right"]
["Bell shape or Symmetrical Shape indicates no skew"]
["Skew"]
["Skew measures the shape of the distribution"]
["Use SKEW function"]
["Left or Negative Skew"]
["A few small values pull the Mean down away from Median"]
["Mean usually less than Median"]
["Right or Positive Skew"]
["A few big values pull the Mean up away from Median"]
["Mean usually greater than Median"]
["Bell Shape or Symmetrical Shape - No Skew"]
["Mean and Median equal"]
["Relative Location = "]
["z Score = Standardized Value"]
["Z score measures the relative location of a particular x in the data set (as compared to the mean), in units of standard deviation."]
["Z Score tells you \"How Many Standard Deviations The Particular Value Is Away From The Mean\". 1? -1? 1.5? 0?"]
["zi <  0, value below mean\nzi >  0, value above mean\nzi = 0, value is equal to mean"]
["Observations in 2 different data sets that have the same z-score are said to have the same relative location or the same number of standard deviations away from the mean."]
["z = (Particular Value - Mean)/StandardDeviation = zi = (xi-Xbar)/s"]
["Use STANDARDIZE function"]
["Proportion of data values that lie between +/- a given number of standard deviations"]
["Chebyshev's Theorem"]
["Russian Mathematician, P.L. Chebyshev (1821-1894)"]
["Chebyshev's Theorem"]
["Allows us to make a statement about the proportion of data values that must be within a specified number of standard deviations of the mean.\nFor example:\n75% of the students must have a test score between 34 and 86"]
["Rule:\nAt least (1 - 1/z^2) of the values in any data set will be\n within z standard deviations of the mean, where z is\n any value greater than 1.\n"]
["Real power of this Theorem:\nApplies to any data set regardless of the shape of the distribution of the data!"]
["At least 0.75, or 75.00%, of the data values must be within z = 2 standard deviations of the mean"]
["At least 0.89, or 89.00%, of the data values must be within z = 3 standard deviations of the mean"]
["At least 0.94, or 94.00%, of the data values must be within z = 4 standard deviations of the mean"]
["Empirical Rule:"]
["When data has a Symmetric Distribution or Normal Distribution or Bell Shaped Distribution, the Empirical Rule Can be sued to determine the percentage of data values that must be within a specified number of standard deviations of the mean"]
["Approximately 68% of the data values will be within 1 Standard Deviation of the Mean"]
["Approximately 95% of the data values will be within 2 Standard Deviations of the Mean"]
["Approximately 99% of the data values (almost all the data) will be within 3 Standard Deviations of Mean"]
["Detecting Outliers"]
["Outlier"]
["An outlier is an unusually small or unusually large value in a data set."]
["+/- 3 Z Scores Outlier Rule"]
["A data value with a z-score less than -3 or greater than +3 might be considered an outlier."]
["   It might be:\n1)an incorrectly recorded data value\n2)a data value that was incorrectly included in the data set\n3) a correctly recorded data value that belongs in\n     the data set"]
["ABS is absolute Value function in Excel (Distance away from zero)"]
["5 Number Summary"]
["5 Number Summary Provides information like:"]
["Minimum Value "]
["Maximum Value"]
["Median"]
["Range, Max - Min"]
["Quartiles"]
["Interquartile Range = Qart 3 - Quart 1 = range for middle 50%"]
["Basis for building Box Plot"]
["Box Plots and Outliers Rule of 1.5"]
["Use 5 Number Summary and create hand draw chart Box Plot:"]
["1) Horizontal Axis lists range of numbers from smallest to biggest"]
["2) Box or rectangle is drawn with box ends at Quartile 1 and Quartile 2"]
["3) Vertical line drawn at Quartile 2 (Median)"]
["4) Vertical lines for Lower and Upper Limits that indicate that anything past is an Outlier.\nLower Limit = Quartile 1 - 1.5*Interquartile Range, or 0.\nUpper Limit = Quartile 3 + 1.5*Interquartile Range\n**Although for this class we will use 1.5, other multiples can be used (see note on \"5 Number & Box Blot\" Worksheet"]
["5) \"Whiskers\", lines, are drawn from Q1 to Min and Q3 to Max"]
["6) Outliers can be drawn as an \"X\", \"dot\" or \"small box\""]
["Example:"]
[]
[]
["Measures of Association or Linear Relationship Between 2 Variables"]
["Covariance"]
["Covariance measures the strength of the linear relationship between 2 variables. Positive values indicate a positive relationship; negative, a negative relationship"]
["Sample Covariance = sxy = (∑(xi - Xbar)*(yi - Ybar))/(n-1)"]
["Use COVARIANCE.S function"]
["Population Covariance = sxy = (∑(xi - µx)*(yi - µy))/N"]
["Use COVARIANCE.P function"]
["Understanding how Covariance works:"]
["Plot a Mean Line for X and a Mean Line for Y to divide chart into 4 quadrants"]
["When preponderance of the \"X-Deviations Times Y-Deviations\" are positive (X-Y markers are quadrant 1 and 3), the line will show a positive liner association or relationship: \"As X Increases, Y Increase\""]
["When preponderance of the \"X-Deviations Times Y-Deviations\" are negative (X-Y markers are quadrant 2 and 4), the line will show a negative liner association or relationship: \"As X Increases, Y decrease\""]
["When preponderance of the \"X-Deviations Times Y-Deviations\" are neither positive or negative (X-Y markers in all quadrants), the line will show no liner association or relationship"]
["Covariance measures the strength of the linear relationship between 2 variables, but has a problem with units.\nBecause the value of covariance depends on units, we may get larger or smaller covariances simply because we change the units from inches to feet or feet to inches.\nTo overcome this problem with units, we can divide the covariance by the product of the standard deviation to the x and y values, sx * sy , to get a relative measure of strength of association or relationship between 2 variables or a measure of strength per unit of standard deviation, sx * sy . This measure is called Correlation Coefficient"]
["Correlation Coefficient = Pearson Product Moment Correlation Coefficient"]
["Correlation Coefficient measure the strength and direction of linear association or relationship."]
["Values range from -1, perfect negative linear relationship (all X-Y markers on  regression line) to +1, perfect positive linear relationship  (all X-Y markers on  regression line) "]
["Values close to +1 show strong positive linear association/relationship\nValues close to -1 show strong negative linear association/relationship\nThe closer to zero the weaker the relationship\nThe closer the X-Y markers are to the regression line, the stronger the relationship\nThe more the X-Y markers deviate from the regression line, the weaker the relationship"]
["Note: Because the Correlation Coefficient measure the strength and direction of a LINEAR relationship, not nonlinear relationships. If you get a correlation measure near zero, it may be true that there is a very weak linear relationship, but that does not say that there is not some other sort of non-linear relationship."]
["Sample Correlation Coefficient = rxy = sxy /(sx * sy)"]
["Use PEARSON or CORREL functions"]
["Population Correlation Coefficient rxy = sxy /(sx * sy) , r is pronounced \"Row\""]
["Use PEARSON or CORREL functions"]
["Sample Correlation Coefficient, rxy , is a point estimator of the Population Correlation Coefficient, rxy"]
["Correlation is not causation."]
[]
["Excel Notes:"]
["Descriptive Statistics Tool to quickly calculate many statistics quickly"]
["Data Ribbon Tab, Analysis group, Data Analysis button, select \"Descriptive Statistics\", in Input Range text box select column of values and Field Name, select dialog button for \"Columns\", check the check box for \"Labels in First Row\", select output location you want, check the check box for \"Summary Statistics\""]
["This feature calculates many statistics quickly, but the output in not linked to the source data cells and will NOT update if source data changes, like formulas would"]
["Excel Box Plot Notes:"]
["1) Calculate 5 number summary\n2) Calculate the difference between each of the values: 1) Min - 0, 2) Q1 - MIN, 3) Q2 - Q1,  4) Q3 - Q3, 5) Max - Q3\n3) Create Stacked Bar Chart\n4) Switch Row and Column\n5) Add fill = \"No Fill\" to 1st two parts of bar and last\n6) Add Error Bar to 2nd part of bar with Minus / Custom / Negative = Quartile 1 - MIN\n7) Add Error Bar to 4th part of bar with Plus / Custom / Positive = Max - Q3\n8) Calculate Mean\n9) Add Mean to Chart\n10) Change that data point to X - Y Scatter\n11) Edit Source data for X - Y Scatter and change X to Mean Value and Y to 1."]

{"sheet":"Location","index":4,"n_rows":21,"n_cols":4,"rows_kept":21,"cut":false,"hidden":false,"merged":[]}
["Test"]
[31,null,"You:",79]
[47]
[54,null,"Are you above average?"]
[55,null,"Mean"]
[57]
[58,null,{"$f":"\"Are you above the \"&D8*100&\"th percentile?\"","v":"Are you above the 75th percentile?"}]
[60,null,{"$f":"\"Percentile Marker for \"&TEXT(D8,\"0%\")","v":"Percentile Marker for 75%"},0.75]
[61,null,{"$f":"\"Marker for \"&D8*100&\"th percentile:\"","v":"Marker for 75th percentile:"}]
[63]
[68]
[68]
[68]
[72]
[76]
[78]
[79]
[84]
[89]
[91]
[95]

{"sheet":"Location (an)","index":5,"n_rows":21,"n_cols":4,"rows_kept":21,"cut":false,"hidden":false,"merged":[]}
["Test"]
[31,null,"You:",79]
[47]
[54,null,"Are you above average?"]
[55,null,"Mean",{"$f":"AVERAGE(A2:A21)","v":67.7}]
[57]
[58,null,{"$f":"\"Are you above the \"&D8*100&\"th percentile?\"","v":"Are you above the 75th percentile?"}]
[60,null,{"$f":"\"Percentile Marker for \"&TEXT(D8,\"0%\")","v":"Percentile Marker for 75%"},0.75]
[61,null,{"$f":"\"Marker for \"&D8*100&\"th percentile:\"","v":"Marker for 75th percentile:"},{"$f":"_xlfn.PERCENTILE.EXC(A2:A21,D8)","v":78.75}]
[63]
[68]
[68]
[68]
[72]
[76]
[78]
[79]
[84]
[89]
[91]
[95]

{"sheet":"Terms","index":6,"n_rows":10,"n_cols":1,"rows_kept":10,"cut":false,"hidden":false,"merged":[]}
["Population: The set of all elements of interest in a particular study"]
["Sample: A subset of the population"]
["Inferential Statistics: The process of using data obtained from a sample to make estimates and test hypotheses about the characteristics/attribute of a population"]
["If the measures are computed for data from a sample, they are called sample statistics.\n"]
["If the measures are computed for data from a population, they are called population parameters.\n"]
["In Statistical Inference, a sample statistic is referred to as the point estimator of the corresponding population parameter.\n"]
["Since it is usually impossible to get all the numbers for a population, we have to use sample statistics to reasonably estimate the population parameter!"]
["Example: calculate an sample average of number of salmon in Puget Sound and use it as the estimate of the population average of number of salmon in Puget Sound - \"We can't count all the fish in the sea!!\""]
[]
["More about Point estimation in chapter 7."]

{"sheet":"MeanPorS","index":7,"n_rows":21,"n_cols":10,"rows_kept":21,"cut":false,"hidden":false,"merged":[[12,3,17,4]]}
["Particular X","Population Data",null,"Particular X","Sample Data"]
["X1",15,null,"X1",{"$f":"INDEX($B$2:$B$16,(ROWS(E$2:E2)-1)*2+1)","v":15}]
["X2",10,null,"X2",{"$f":"INDEX($B$2:$B$16,(ROWS(E$2:E3)-1)*2+1)","v":14}]
["X3",14,null,"X3",{"$f":"INDEX($B$2:$B$16,(ROWS(E$2:E4)-1)*2+1)","v":14}]
["X4",13,null,"X4",{"$f":"INDEX($B$2:$B$16,(ROWS(E$2:E5)-1)*2+1)","v":11}]
["X5",14,null,"X5",{"$f":"INDEX($B$2:$B$16,(ROWS(E$2:E6)-1)*2+1)","v":10}]
["X6",8,null,"X6",{"$f":"INDEX($B$2:$B$16,(ROWS(E$2:E7)-1)*2+1)","v":9},null,"Xbar"," =","Sample Mean","\"Xbar\""]
["X7",11,null,"X7",{"$f":"INDEX($B$2:$B$16,(ROWS(E$2:E8)-1)*2+1)","v":9},null,"xi"," =","Particular Value","\"x sub i\""]
["X8",11,null,"X8",{"$f":"INDEX($B$2:$B$16,(ROWS(E$2:E9)-1)*2+1)","v":6},null,"∑"," =","Greek Letter used for \"adding\"","\"Sigma\""]
["X9",10,null,null,null,null,"n"," =","Count of sample items","\"n\""]
["X10",13,null,"Xbar = "]
["X11",9]
["X12",11,null,{"$f":"IF(E11=\"\",\"\",\"The Sample Statistic 'Sample Mean Xbar', \"&DOLLAR(E11)&\" is the point estimate of the Population Parameter 'Population Mean Mew'\")","v":""}]
["X13",9]
["X14",9]
["X15",6]
[]
["Mew = μ = ",null,null,null,null,null,"µ"," =","Population Mean","\"Mew\""]
[null,null,null,null,null,null,"xi"," =","Particular Value","\"x sub i\""]
[null,null,null,null,null,null,"∑"," =","Greek Letter used for \"adding\"","\"Sigma\""]
[null,null,null,null,null,null,"N"," =","Count of population items","\"N\""]

{"sheet":"MeanPorS (an)","index":8,"n_rows":21,"n_cols":10,"rows_kept":21,"cut":false,"hidden":false,"merged":[[12,3,17,4]]}
["Particular X","Population Data",null,"Particular X","Sample Data"]
["X1",15,null,"X1",{"$f":"INDEX($B$2:$B$16,(ROWS(E$2:E2)-1)*2+1)","v":15}]
["X2",10,null,"X2",{"$f":"INDEX($B$2:$B$16,(ROWS(E$2:E3)-1)*2+1)","v":14}]
["X3",14,null,"X3",{"$f":"INDEX($B$2:$B$16,(ROWS(E$2:E4)-1)*2+1)","v":14}]
["X4",13,null,"X4",{"$f":"INDEX($B$2:$B$16,(ROWS(E$2:E5)-1)*2+1)","v":11}]
["X5",14,null,"X5",{"$f":"INDEX($B$2:$B$16,(ROWS(E$2:E6)-1)*2+1)","v":10}]
["X6",8,null,"X6",{"$f":"INDEX($B$2:$B$16,(ROWS(E$2:E7)-1)*2+1)","v":9},null,"Xbar"," =","Sample Mean","\"Xbar\""]
["X7",11,null,"X7",{"$f":"INDEX($B$2:$B$16,(ROWS(E$2:E8)-1)*2+1)","v":9},null,"xi"," =","Particular Value","\"x sub i\""]
["X8",11,null,"X8",{"$f":"INDEX($B$2:$B$16,(ROWS(E$2:E9)-1)*2+1)","v":6},null,"∑"," =","Greek Letter used for \"adding\"","\"Sigma\""]
["X9",10,null,null,null,null,"n"," =","Count of sample items","\"n\""]
["X10",13,null,"Xbar = ",{"$f":"AVERAGE(E2:E9)","v":11}]
["X11",9]
["X12",11,null,{"$f":"IF(E11=\"\",\"\",\"The Sample Statistic 'Sample Mean Xbar', \"&DOLLAR(E11)&\" is the point estimate of the Population Parameter 'Population Mean Mew'\")","v":"The Sample Statistic 'Sample Mean Xbar', $11.00 is the point estimate of the Population Parameter 'Population Mean Mew'"}]
["X13",9]
["X14",9]
["X15",6]
[]
["Mew = μ = ",{"$f":"AVERAGE(B2:B16)","v":10.866666666666667},null,null,null,null,"µ"," =","Population Mean","\"Mew\""]
[null,null,null,null,null,null,"xi"," =","Particular Value","\"x sub i\""]
[null,null,null,null,null,null,"∑"," =","Greek Letter used for \"adding\"","\"Sigma\""]
[null,null,null,null,null,null,"N"," =","Count of population items","\"N\""]

{"sheet":"Mean","index":9,"n_rows":33,"n_cols":4,"rows_kept":33,"cut":false,"hidden":false,"merged":[]}
[]
[]
[]
[]
[]
[]
[]
[]
[]
["Mean Test Scores",null,null,"Mean Weights (oz.)"]
[{"$f":"IF(B10<>\"\",\"We can use this average to compare to our scores and gage how well we did.\",\"\")","v":""},null,null,{"$f":"IF(E10<>\"\",\"We can use this average to help us estimate whether the machine is filling correctly.\",\"\")","v":""}]
[]
["Test Scores (Population)",null,null,"Weights (oz.) of Salad Bag from Filling Machine (Sample)"]
[60,null,null,10.8]
[68,null,null,10.74]
[76,null,null,10.32]
[84,null,null,9.73]
[55,null,null,9.15]
[63,null,null,10.94]
[89,null,null,10.83]
[31,null,null,9.36]
[47,null,null,10.42]
[68,null,null,9.9700000000000006]
[54,null,null,10.23]
[58,null,null,9.83]
[57,null,null,10.41]
[61,null,null,10.35]
[72,null,null,9.8699999999999992]
[91,null,null,10.02]
[95,null,null,9.4499999999999993]
[68,null,null,10.46]
[76,null,null,10.8]
[76,null,null,10.01]

{"sheet":"Mean (an)","index":10,"n_rows":33,"n_cols":5,"rows_kept":33,"cut":false,"hidden":false,"merged":[]}
[]
[]
[]
[]
[]
[]
[]
[]
[]
["Mean Test Scores",{"$f":"AVERAGE(A14:A33)","v":67.45},null,"Mean Weights (oz.)",{"$f":"AVERAGE(D14:D33)","v":10.184500000000002}]
[{"$f":"IF(B10<>\"\",\"We can use this average to compare to our scores and gage how well we did.\",\"\")","v":"We can use this average to compare to our scores and gage how well we did."},null,null,{"$f":"IF(E10<>\"\",\"We can use this average to help us estimate whether the machine is filling correctly.\",\"\")","v":"We can use this average to help us estimate whether the machine is filling correctly."}]
[]
["Test Scores (Population)",null,null,"Weights (oz.) of Salad Bag from Filling Machine (Sample)"]
[60,null,null,10.8]
[68,null,null,10.74]
[76,null,null,10.32]
[84,null,null,9.73]
[55,null,null,9.15]
[63,null,null,10.94]
[89,null,null,10.83]
[31,null,null,9.36]
[47,null,null,10.42]
[68,null,null,9.9700000000000006]
[54,null,null,10.23]
[58,null,null,9.83]
[57,null,null,10.41]
[61,null,null,10.35]
[72,null,null,9.8699999999999992]
[91,null,null,10.02]
[95,null,null,9.4499999999999993]
[68,null,null,10.46]
[76,null,null,10.8]
[76,null,null,10.01]

{"sheet":"Median","index":11,"n_rows":36,"n_cols":13,"rows_kept":36,"cut":false,"hidden":false,"merged":[]}
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
["Mean House Price",null,null,null,null,null,null,null,"Mean Salary",null,null,null,"Median can be a better measure of central location when there are extreme values"]
["Median House Price",null,null,null,null,null,null,null,"Median Salary"]
[{"$f":"IF(C12<>\"\",\"Median gives us a more representative 'typical value' to use in discussions and ads - because it is not affected by extreme values.\",\"\")","v":""},null,null,null,null,null,null,null,{"$f":"IF(K12<>\"\",\"Median gives us a more representative 'typical value' to make your decision. As soon as you see that the mean is larger than the median, you know that a few large values are pulling the mean up.\",\"\")","v":""}]
[]
["House Prices",null,"House Prices",null,null,null,null,null,"Salaries at Accounting Firm",null,"Salaries at Accounting Firm"]
[297873,null,297873,null,null,null,null,null,103332,null,103332]
[170143,null,170143,null,null,null,null,null,120102,null,120102]
[109159,null,109159,null,null,null,null,null,144354,null,144354]
[277309,null,277309,null,null,null,null,null,138409,null,138409]
[239136,null,239136,null,null,null,null,null,93949,null,93949]
[114460,null,114460,null,null,null,null,null,59915,null,59915]
[224649,null,224649,null,null,null,null,null,54594,null,54594]
[99857,null,99857,null,null,null,null,null,92784,null,92784]
[144686,null,144686,null,null,null,null,null,550250,null,550250]
[193308,null,193308,null,null,null,null,null,59196,null,59196]
[144244,null,144244,null,null,null,null,null,58072,null,58072]
[118492,null,118492,null,null,null,null,null,51671,null,51671]
[178708,null,178708,null,null,null,null,null,119001,null,119001]
[750022,null,750022,null,null,null,null,null,126737,null,126737]
[143400,null,143400,null,null,null,null,null,105127,null,105127]
[201661,null,201661,null,null,null,null,null,139330,null,139330]
[1258963,null,1258963,null,null,null,null,null,109151,null,109151]
[198345,null,198345,null,null,null,null,null,119831,null,119831]
[394136,null,394136,null,null,null,null,null,255800,null,255800]
[2589657,null,2589657,null,null,null,null,null,790500,null,790500]
[null,null,null,null,null,null,null,null,96842,null,96842]

{"sheet":"Median (an)","index":12,"n_rows":36,"n_cols":13,"rows_kept":36,"cut":false,"hidden":false,"merged":[]}
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
["Mean House Price",null,{"$f":"AVERAGE(A16:A35)","v":392410.4},null,null,null,null,null,"Mean Salary",null,{"$f":"AVERAGE(I16:I36)","v":161378.42857142858},null,"Median can be a better measure of central location when there are extreme values"]
["Median House Price",null,{"$f":"MEDIAN(A16:A35)","v":195826.5},{"$f":"AVERAGE(C25:C26)","v":195826.5},null,null,null,null,"Median Salary",null,{"$f":"MEDIAN(I16:I36)","v":109151}]
[{"$f":"IF(C12<>\"\",\"Median gives us a more representative 'typical value' to use in discussions and ads - because it is not affected by extreme values.\",\"\")","v":"Median gives us a more representative 'typical value' to use in discussions and ads - because it is not affected by extreme values."},null,null,null,null,null,null,null,{"$f":"IF(K12<>\"\",\"Median gives us a more representative 'typical value' to make your decision. As soon as you see that the mean is larger than the median, you know that a few large values are pulling the mean up.\",\"\")","v":"Median gives us a more representative 'typical value' to make your decision. As soon as you see that the mean is larger than the median, you know that a few large values are pulling the mean up."}]
[]
["House Prices",null,"House Prices",null,null,null,null,null,"Salaries at Accounting Firm",null,"Salaries at Accounting Firm"]
[297873,null,99857,null,null,null,null,null,103332,null,51671]
[170143,null,109159,null,null,null,null,null,120102,null,54594]
[109159,null,114460,null,null,null,null,null,144354,null,58072]
[277309,null,118492,null,null,null,null,null,138409,null,59196]
[239136,null,143400,null,null,null,null,null,93949,null,59915]
[114460,null,144244,null,null,null,null,null,59915,null,92784]
[224649,null,144686,null,null,null,null,null,54594,null,93949]
[99857,null,170143,null,null,null,null,null,92784,null,96842]
[144686,null,178708,null,null,null,null,null,550250,null,103332]
[193308,null,193308,null,null,null,null,null,59196,null,105127]
[144244,null,198345,null,null,null,null,null,58072,null,109151]
[118492,null,201661,null,null,null,null,null,51671,null,119001]
[178708,null,224649,null,null,null,null,null,119001,null,119831]
[750022,null,239136,null,null,null,null,null,126737,null,120102]
[143400,null,277309,null,null,null,null,null,105127,null,126737]
[201661,null,297873,null,null,null,null,null,139330,null,138409]
[1258963,null,394136,null,null,null,null,null,109151,null,139330]
[198345,null,750022,null,null,null,null,null,119831,null,144354]
[394136,null,1258963,null,null,null,null,null,255800,null,255800]
[2589657,null,2589657,null,null,null,null,null,790500,null,550250]
[null,null,null,null,null,null,null,null,96842,null,790500]

{"sheet":"Mode","index":13,"n_rows":50,"n_cols":7,"rows_kept":50,"cut":false,"hidden":false,"merged":[]}
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
["Rate Your Professor","Frequency"]
["Bad",null,"<< Mode is one that occurs most frequently",null,null,null,"See this video for more about MODE.MULT"]
["Okay",null,null,null,null,null,"Excel Magic Trick 973: List Multiple MODEs in Excel 2007 or earlier: COUNTIF or PivotTable "]
["Good",null,null,null,null,null,"https://www.youtube.com/watch?v=NjV4BZZRAQY"]
["Great",null,null,"Mode For Test Data",null,null,"Excel Magic Trick 1118: MODE for Text Values, Extract Word That Occurs Most Frequently "]
[{"$f":"IF(AND(B13:B16<>\"\"),\"With Nominal or Ordinal Data we have a way of calculating a 'Typical Value'.\",\"\")","v":""},null,null,{"$f":"IF(E16<>\"\",\"MODE.SNGL will not tell you if there is more than one Mode. MODE.SNGL will just report the first one it encounters. A quick PivotTable will help reveal if there is more than one Mode.\",\"\")","v":""},null,null,"https://www.youtube.com/watch?v=pki8C60Diz0"]
[]
["Rate Your Professor",null,null,"Test Scores (Population)"]
["Okay",null,null,60,null,null,"Scores on Quiz"]
["Good",null,null,68,null,null,6]
["Great",null,null,76,null,null,3]
["Bad",null,null,84,null,null,5]
["Bad",null,null,55,null,null,3]
["Bad",null,null,63,null,null,0]
["Bad",null,null,89,null,null,1]
["Good",null,null,31,null,null,8]
["Okay",null,null,47,null,null,5]
["Okay",null,null,68,null,null,5]
["Okay",null,null,54,null,null,1]
["Okay",null,null,58,null,null,6]
["Okay",null,null,57,null,null,7]
["Bad",null,null,61,null,null,0]
["Bad",null,null,72,null,null,2]
["Bad",null,null,91,null,null,6]
["Great",null,null,95,null,null,7]
["Good",null,null,68,null,null,3]
["Good",null,null,76,null,null,4]
["Bad",null,null,76,null,null,9]
[null,null,null,null,null,null,7]
[null,null,null,null,null,null,1]
[null,null,null,null,null,null,4]
[null,null,null,null,null,null,3]
[null,null,null,null,null,null,6]
[null,null,null,null,null,null,7]
[null,null,null,null,null,null,8]
[null,null,null,null,null,null,10]
[null,null,null,null,null,null,5]
[null,null,null,null,null,null,2]
[null,null,null,null,null,null,7]

{"sheet":"Mode (an)","index":14,"n_rows":61,"n_cols":10,"rows_kept":61,"cut":false,"hidden":false,"merged":[]}
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
["Rate Your Professor","Frequency"]
["Bad",{"$f":"COUNTIFS($A$20:$A$39,A13)","v":8},"<< Mode is one that occurs most frequently",null,null,null,"See this video for more about MODE.MULT"]
["Okay",{"$f":"COUNTIFS($A$20:$A$39,A14)","v":6},null,null,null,null,"Excel Magic Trick 973: List Multiple MODEs in Excel 2007 or earlier: COUNTIF or PivotTable "]
["Good",4,null,null,null,null,"https://www.youtube.com/watch?v=NjV4BZZRAQY"]
["Great",2,null,"Mode For Test Data",{"$f":"_xlfn.MODE.SNGL(D20:D39)","v":68},null,"Excel Magic Trick 1118: MODE for Text Values, Extract Word That Occurs Most Frequently "]
[{"$f":"IF(AND(B13:B16<>\"\"),\"With Nominal or Ordinal Data we have a way of calculating a 'Typical Value'.\",\"\")","v":"With Nominal or Ordinal Data we have a way of calculating a 'Typical Value'."},null,null,{"$f":"IF(E16<>\"\",\"MODE.SNGL will not tell you if there is more than one Mode. MODE.SNGL will just report the first one it encounters. A quick PivotTable will help reveal if there is more than one Mode.\",\"\")","v":"MODE.SNGL will not tell you if there is more than one Mode. MODE.SNGL will just report the first one it encounters. A quick PivotTable will help reveal if there is more than one Mode."},null,null,"https://www.youtube.com/watch?v=pki8C60Diz0"]
[]
["Rate Your Professor",null,null,"Test Scores (Population)"]
["Okay",null,null,60,null,null,"Scores on Quiz"]
["Good",null,null,68,null,null,6,null,"Scores on Quiz","Count of Scores on Quiz"]
["Great",null,null,76,null,null,3,null,0,2]
["Bad",null,null,84,null,null,5,null,1,3]
["Bad",null,null,55,null,null,3,null,2,2]
["Bad",null,null,63,null,null,0,null,3,4]
["Bad",null,null,89,null,null,1,null,4,2]
["Good",null,null,31,null,null,8,null,5,4]
["Okay",null,null,47,null,null,5,null,6,4]
["Okay",null,null,68,null,null,5,null,7,5]
["Okay",null,null,54,null,null,1,null,8,2]
["Okay",null,null,58,null,null,6,null,9,1]
["Okay",null,null,57,null,null,7,null,10,1]
["Bad",null,null,61,null,null,0,null,"Grand Total",30]
["Bad",null,null,72,null,null,2]
["Bad",null,null,91,null,null,6]
["Great",null,null,95,null,null,7]
["Good",null,null,68,null,null,3]
["Good",null,null,76,null,null,4]
["Bad",null,null,76,null,null,9]
[null,null,null,null,null,null,7]
[null,null,null,null,null,null,1]
[null,null,null,null,null,null,4]
[null,null,null,null,null,null,3]
[null,null,null,"Test Scores","Count of Test Scores (Population)",null,6]
[null,null,null,31,1,null,7]
[null,null,null,47,1,null,8]
[null,null,null,54,1,null,10]
[null,null,null,55,1,null,5]
[null,null,null,57,1,null,2]
[null,null,null,58,1,null,7]
[null,null,null,60,1]
[null,null,null,61,1]
[null,null,null,63,1]
[null,null,null,68,3]
[null,null,null,72,1]
[null,null,null,76,3]
[null,null,null,84,1]
[null,null,null,89,1]
[null,null,null,91,1]
[null,null,null,95,1]
[null,null,null,"Grand Total",20]

{"sheet":"Skew","index":15,"n_rows":44,"n_cols":11,"rows_kept":44,"cut":false,"hidden":false,"merged":[]}
[null,"Skew tells to which way the distribution is tilting. If the average is being pulled up by a few big values, the skew is positive, or to the right. If the average is being pulled down by a few small values, the skew is negative, or to the left. Range of Skew is between -3 and 3."]
[null,"If the Skew is 0, the Mean = Median = Mode = Normal or Bell Distribution"]
[null,"We can use the SKEW function to calculate the Skew"]
[]
[]
[null,null,null,null,null,null,null,"Xbar"," =","Sample Mean","\"Xbar\""]
[null,null,null,null,null,null,null,"xi"," =","Particular Value","\"x sub i\""]
[null,null,null,null,null,null,null,"∑"," =","Greek Letter used for \"adding\"","\"Sigma\""]
[null,null,null,null,null,null,null,"n"," =","Count of sample items","\"n\""]
[null,null,null,null,null,null,null,"s"," =","Sample Standard Deviation","\"s\""]
[null,"Moderate Skew","No Skew","Highly Skewed"]
[null,"Negative (Left) Skew\nsmall values pull Mean down","No Skew (Normal or Bell)\nMean = Median = Mode","Positive (Right) Skew\nbig values pull Mean up"]
["count = n",{"$f":"COUNT(B21:B30)","v":10},{"$f":"COUNT(C21:C30)","v":10},{"$f":"COUNT(D21:D30)","v":10}]
["Skew",{"$f":"B13/((B13-1)*(B13-2))*SUMPRODUCT(((B21:B30-AVERAGE(B21:B30))/_xlfn.STDEV.S(B21:B30))^3)","v":-0.64052176797398908},{"$f":"C13/((C13-1)*(C13-2))*SUMPRODUCT(((C21:C30-AVERAGE(C21:C30))/_xlfn.STDEV.S(C21:C30))^3)","v":-1.1277285554160596E-2},{"$f":"D13/((D13-1)*(D13-2))*SUMPRODUCT(((D21:D30-AVERAGE(D21:D30))/_xlfn.STDEV.S(D21:D30))^3)","v":1.5524906262931217}]
["Skew",{"$f":"SKEW(B21:B30)","v":-0.64052176797398896},-1.1277285554160533E-2,1.5524906262931217]
["Mean"]
["Median"]
["Mode"]
[null,"Moderate Skew","No Skew","Highly Skewed"]
[null,"Data 1","Data 2","Data 3"]
[null,7,7.9,1]
[null,43,15,7]
[null,33,11,7]
[null,21,21,12]
[null,15,25,11]
[null,31,25,14]
[null,36,27,2]
[null,43,35,1.9]
[null,48,38,39]
[null,49,41,49]
[]
["Labels","Frequency","Frequency","Frequency"]
[{"$f":"A40&\"-\"&B40-1","v":"0-9"},{"$f":"FREQUENCY(B21:B30,B40:B44)","v":1},{"$f":"FREQUENCY(C21:C30,C40:C44)","v":1},{"$f":"FREQUENCY(D21:D30,D40:D44)","v":5}]
[{"$f":"A41&\"-\"&B41-1","v":"10-19"},1,2,3]
[{"$f":"A42&\"-\"&B42-1","v":"20-29"},1,4,0]
[{"$f":"A43&\"-\"&B43-1","v":"30-39"},3,2,1]
[{"$f":"A44&\"-\"&B44-1","v":"40-49"},4,1,1]
[]
["Low","Bins","Bins","Bins"]
[{"$f":"B40-10","v":0},10,10,10]
[{"$f":"B41-10","v":10},20,20,20]
[{"$f":"B42-10","v":20},30,30,30]
[{"$f":"B43-10","v":30},40,40,40]
[40,50,50,50]

{"sheet":"Skew (an)","index":16,"n_rows":44,"n_cols":11,"rows_kept":44,"cut":false,"hidden":false,"merged":[]}
[null,"Skew tells to which way the distribution is tilting. If the average is being pulled up by a few big values, the skew is positive, or to the right. If the average is being pulled down by a few small values, the skew is negative, or to the left. Range of Skew is between -3 and 3."]
[null,"If the Skew is 0, the Mean = Median = Mode = Normal or Bell Distribution"]
[null,"We can use the SKEW function to calculate the Skew"]
[]
[]
[null,null,null,null,null,null,null,"Xbar"," =","Sample Mean","\"Xbar\""]
[null,null,null,null,null,null,null,"xi"," =","Particular Value","\"x sub i\""]
[null,null,null,null,null,null,null,"∑"," =","Greek Letter used for \"adding\"","\"Sigma\""]
[null,null,null,null,null,null,null,"n"," =","Count of sample items","\"n\""]
[null,null,null,null,null,null,null,"s"," =","Sample Standard Deviation","\"s\""]
[null,"Moderate Skew","No Skew","Highly Skewed"]
[null,"Negative (Left) Skew\nsmall values pull Mean down","No Skew (Normal or Bell)\nMean = Median = Mode","Positive (Right) Skew\nbig values pull Mean up"]
["count = n",{"$f":"COUNT(B21:B30)","v":10},{"$f":"COUNT(C21:C30)","v":10},{"$f":"COUNT(D21:D30)","v":10}]
["Skew",{"$f":"B13/((B13-1)*(B13-2))*SUMPRODUCT(((B21:B30-AVERAGE(B21:B30))/_xlfn.STDEV.S(B21:B30))^3)","v":-0.64052176797398908},{"$f":"C13/((C13-1)*(C13-2))*SUMPRODUCT(((C21:C30-AVERAGE(C21:C30))/_xlfn.STDEV.S(C21:C30))^3)","v":-1.1277285554160596E-2},{"$f":"D13/((D13-1)*(D13-2))*SUMPRODUCT(((D21:D30-AVERAGE(D21:D30))/_xlfn.STDEV.S(D21:D30))^3)","v":1.5524906262931217}]
["Skew",{"$f":"SKEW(B21:B30)","v":-0.64052176797398896},-1.1277285554160533E-2,1.5524906262931217]
["Mean",{"$f":"AVERAGE(B21:B30)","v":32.6},{"$f":"AVERAGE(C21:C30)","v":24.59},14.39]
["Median",{"$f":"MEDIAN(B21:B30)","v":34.5},{"$f":"MEDIAN(C21:C30)","v":25},9]
["Mode",{"$f":"_xlfn.MODE.SNGL(B21:B30)","v":43},{"$f":"_xlfn.MODE.SNGL(C21:C30)","v":25},7]
[null,"Moderate Skew","No Skew","Highly Skewed"]
[null,"Data 1","Data 2","Data 3"]
[null,7,7.9,1]
[null,43,15,7]
[null,33,11,7]
[null,21,21,12]
[null,15,25,11]
[null,31,25,14]
[null,36,27,2]
[null,43,35,1.9]
[null,48,38,39]
[null,49,41,49]
[]
["Labels","Frequency","Frequency","Frequency"]
[{"$f":"A40&\"-\"&B40-1","v":"0-9"},{"$f":"FREQUENCY(B21:B30,B40:B44)","v":1},{"$f":"FREQUENCY(C21:C30,C40:C44)","v":1},{"$f":"FREQUENCY(D21:D30,D40:D44)","v":5}]
[{"$f":"A41&\"-\"&B41-1","v":"10-19"},1,2,3]
[{"$f":"A42&\"-\"&B42-1","v":"20-29"},1,4,0]
[{"$f":"A43&\"-\"&B43-1","v":"30-39"},3,2,1]
[{"$f":"A44&\"-\"&B44-1","v":"40-49"},4,1,1]
[]
["Low","Bins","Bins","Bins"]
[{"$f":"B40-10","v":0},10,10,10]
[{"$f":"B41-10","v":10},20,20,20]
[{"$f":"B42-10","v":20},30,30,30]
[{"$f":"B43-10","v":30},40,40,40]
[40,50,50,50]

{"sheet":"Weighted Mean","index":17,"n_rows":22,"n_cols":10,"rows_kept":22,"cut":false,"hidden":false,"merged":[]}
[]
[]
[]
[]
[]
[]
[]
["Quiz Scores (list of scores)",null,"Unique List of Quiz Scores, xi","Frequency, wi","wi * xi = M1"]
[5,null,5,{"$f":"COUNTIFS($A$9:$A$20,C9)","v":2}]
[6,null,6,{"$f":"COUNTIFS($A$9:$A$20,C10)","v":3}]
[8,null,8,3]
[10,null,10,2]
[2,null,2,1,null,null,"Xbar"," =","Sample Mean","\"Xbar\""]
[0,null,0,1,null,null,"xi"," =","Particular Value","\"x sub i\""]
[5,null,"Total",null,null,null,"wi"," =","Particular Weight","\"w sub i\""]
[6,null,null,null,null,null,"∑"," =","Greek Letter used for \"adding\"","\"Sigma\""]
[8,null,"Mean M1",null,null,null,"n"," =","Count of sample items","\"n\""]
[6,null,"Mean M2"]
[10]
[8]
[]
["Mean"]

{"sheet":"Weighted Mean (an)","index":18,"n_rows":22,"n_cols":10,"rows_kept":22,"cut":false,"hidden":false,"merged":[]}
[]
[]
[]
[]
[]
[]
[]
["Quiz Scores (list of scores)",null,"Unique List of Quiz Scores, xi","Frequency, wi","wi * xi = M1"]
[5,null,5,{"$f":"COUNTIFS($A$9:$A$20,C9)","v":2},{"$f":"D9*C9","v":10}]
[6,null,6,{"$f":"COUNTIFS($A$9:$A$20,C10)","v":3},18]
[8,null,8,3,24]
[10,null,10,2,20]
[2,null,2,1,2,null,"Xbar"," =","Sample Mean","\"Xbar\""]
[0,null,0,1,0,null,"xi"," =","Particular Value","\"x sub i\""]
[5,null,"Total",{"$f":"SUM(D9:D14)","v":12},{"$f":"SUM(E9:E14)","v":74},null,"wi"," =","Particular Weight","\"w sub i\""]
[6,null,null,null,null,null,"∑"," =","Greek Letter used for \"adding\"","\"Sigma\""]
[8,null,"Mean M1",{"$f":"E15/D15","v":6.166666666666667},null,null,"n"," =","Count of sample items","\"n\""]
[6,null,"Mean M2",{"$f":"SUMPRODUCT(C9:C14,D9:D14)/SUM(D9:D14)","v":6.166666666666667}]
[10]
[8]
[]
["Mean",{"$f":"AVERAGE(A9:A20)","v":6.166666666666667}]

{"sheet":"WM(2)","index":19,"n_rows":31,"n_cols":27,"rows_kept":31,"cut":false,"hidden":false,"merged":[]}
["Purchases Last Year of Quad Boomerangs",null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Price per Unit"]
["The Accounting Department needs the weighted average to calculate the End Inventory",null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,{"$f":"ROUND(43/2*(1+RANDBETWEEN(-20,30)/100),2)","v":22.79}]
[]
["Date","Quantity Purchased wi","Price per Unit xi","wi * xi \n= M1"]
["2014-01-02",108,24.3,null,null,"Weighted Average M1"]
["2014-02-15",72,26.45,null,null,"Weighted Average M2"]
["2014-03-28",120,26.45,null,null,"Units Sold in 2014",865]
["2014-04-27",108,17.420000000000002,null,null,"COGS"]
["2014-05-19",36,26.02,null,null,"End Inventory"]
["2014-06-19",96,21.72]
["2014-07-22",36,20.64,null,null,"Total",null,"Check"]
["2014-09-04",120,25.37]
["2014-09-26",36,23.44]
["2014-11-03",144,21.72]
["2014-12-03",144,20.21]
["Total",{"$f":"SUM(B5:B15)","v":1020}]
[]
[]
["Price per Unit","Frequency","Percent Frequency","M3",null,"Weighted Average M3"]
[24.3,108,null,null,null,"Weighted Average M4"]
[26.45,72]
[26.45,120,null,null,null,"Chapter 5, they will call this sort of Weighted"]
[17.420000000000002,108,null,null,null,"Average an \"Expected Value\""]
[26.02,36]
[21.72,96]
[20.64,36]
[25.37,120]
[23.44,36]
[21.72,144]
[20.21,144]
[null,{"$f":"SUM(B20:B30)","v":1020}]

{"sheet":"WM(2an)","index":20,"n_rows":31,"n_cols":27,"rows_kept":31,"cut":false,"hidden":false,"merged":[]}
["Purchases Last Year of Quad Boomerangs",null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Price per Unit"]
["The Accounting Department needs the weighted average to calculate the End Inventory",null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,{"$f":"ROUND(43/2*(1+RANDBETWEEN(-20,30)/100),2)","v":21.5}]
[]
["Date","Quantity Purchased wi","Price per Unit xi","wi * xi \n= M1"]
["2014-01-02",108,24.3,{"$f":"B5*C5","v":2624.4},null,"Weighted Average M1",{"$f":"D16/B16","v":22.81882352941177}]
["2014-02-15",72,26.45,{"$f":"B6*C6","v":1904.3999999999999},null,"Weighted Average M2",{"$f":"SUMPRODUCT(C5:C15,B5:B15)/SUM(B5:B15)","v":22.81882352941177}]
["2014-03-28",120,26.45,3174,null,"Units Sold in 2014",865]
["2014-04-27",108,17.420000000000002,1881.3600000000001,null,"COGS",{"$f":"G7*G6","v":19738.28235294118}]
["2014-05-19",36,26.02,936.72,null,"End Inventory",{"$f":"(B16-G7)*G6","v":3536.9176470588245}]
["2014-06-19",96,21.72,2085.12]
["2014-07-22",36,20.64,743.04,null,"Total",{"$f":"SUM(G8:G9)","v":23275.200000000004},"Check"]
["2014-09-04",120,25.37,3044.4]
["2014-09-26",36,23.44,843.84]
["2014-11-03",144,21.72,3127.68]
["2014-12-03",144,20.21,2910.2400000000002]
["Total",{"$f":"SUM(B5:B15)","v":1020},null,{"$f":"SUM(D5:D15)","v":23275.200000000004}]
[]
[]
["Price per Unit","Frequency","Percent Frequency","M3",null,"Weighted Average M3",{"$f":"D31","v":22.818823529411763}]
[24.3,108,{"$f":"B20/$B$31","v":0.10588235294117647},{"$f":"C20*A20","v":2.5729411764705881},null,"Weighted Average M4",{"$f":"SUMPRODUCT(A20:A30,C20:C30)","v":22.818823529411763}]
[26.45,72,{"$f":"B21/$B$31","v":7.0588235294117646E-2},{"$f":"C21*A21","v":1.8670588235294117}]
[26.45,120,0.11764705882352941,3.111764705882353,null,"Chapter 5, they will call this sort of Weighted"]
[17.420000000000002,108,0.10588235294117647,1.8444705882352943,null,"Average an \"Expected Value\""]
[26.02,36,3.5294117647058823E-2,0.91835294117647059]
[21.72,96,9.4117647058823528E-2,2.0442352941176467]
[20.64,36,3.5294117647058823E-2,0.72847058823529409]
[25.37,120,0.11764705882352941,2.9847058823529413]
[23.44,36,3.5294117647058823E-2,0.82729411764705885]
[21.72,144,0.14117647058823529,3.0663529411764703]
[20.21,144,0.14117647058823529,2.8531764705882354]
[null,{"$f":"SUM(B20:B30)","v":1020},{"$f":"SUM(C20:C30)","v":0.99999999999999978},{"$f":"SUM(D20:D30)","v":22.818823529411763}]

{"sheet":"GroupedDataMean","index":21,"n_rows":201,"n_cols":16,"rows_kept":201,"cut":false,"hidden":false,"merged":[]}
["Age",null,null,null,"Quantitative: Integer, COUNTIFS",null,null,null,null,null,null,null,null,null,null,"Increment For FD"]
[21,null,"Lower","Upper","Age","Frequency",null,"Midpoint",null,null,null,null,null,"Min",null,19]
[57,null,0,{"$f":"C3+$P$2","v":19},{"$f":"C3&\" - \"&D3","v":"0 - 19"},{"$f":"COUNTIFS($A$2:$A$201,\">=\"&C3,$A$2:$A$201,\"<=\"&D3)","v":13},null,{"$f":"AVERAGE(C3:D3)","v":9.5},null,null,null,null,null,{"$f":"MIN($A$2:$A$201)","v":18}]
[25,null,{"$f":"D3+1","v":20},{"$f":"C4+$P$2","v":39},{"$f":"C4&\" - \"&D4","v":"20 - 39"},{"$f":"COUNTIFS($A$2:$A$201,\">=\"&C4,$A$2:$A$201,\"<=\"&D4)","v":110},null,{"$f":"AVERAGE(C4:D4)","v":29.5},null,null,null,null,null,"Max"]
[38,null,{"$f":"D4+1","v":40},{"$f":"C5+$P$2","v":59},{"$f":"C5&\" - \"&D5","v":"40 - 59"},{"$f":"COUNTIFS($A$2:$A$201,\">=\"&C5,$A$2:$A$201,\"<=\"&D5)","v":56},null,{"$f":"AVERAGE(C5:D5)","v":49.5},null,null,null,null,null,{"$f":"MAX($A$2:$A$201)","v":65}]
[22,null,{"$f":"D5+1","v":60},{"$f":"C6+$P$2","v":79},{"$f":"C6&\" - \"&D6","v":"60 - 79"},{"$f":"COUNTIFS($A$2:$A$201,\">=\"&C6,$A$2:$A$201,\"<=\"&D6)","v":21},null,{"$f":"AVERAGE(C6:D6)","v":69.5}]
[29,null,null,null,"Total",{"$f":"SUM(F3:F6)","v":200}]
[18]
[64,null,null,null,"Average Age",{"$f":"AVERAGE(A2:A201)","v":37.08}]
[27]
[22,null,null,null,"Average Age from Grouped Data",{"$f":"SUMPRODUCT(F3:F6,H3:H6)/F7","v":38}]
[39,null,null,null,"** will not be the same as actual mean because we are"]
[61,null,null,null,"estimating the data points when we use midpoint for the"]
[18,null,null,null,"unknown data points"]
[22]
[52]
[22]
[18]
[55]
[20]
[42]
[30]
[64]
[63]
[41]
[21]
[63]
[54]
[18]
[26]
[59]
[30]
[64]
[30]
[41]
[23]
[52]
[18]
[32]
[22]
[29]
[25]
[63]
[26]
[31]
[23]
[42]
[28]
[23]
[19]
[37]
[38]
[50]
[26]
[33]
[59]
[32]
[27]
[54]
[63]
[48]
[29]
[38]
[24]
[51]
[25]
[65]
[35]
[35]
[21]
[47]
[62]
[63]
[23]
[59]
[48]
[57]
[19]
[48]
[21]
[40]
[25]
[59]
[57]
[56]
[19]
[54]
[65]
[25]
[30]
[57]
[43]
[44]
[21]
[20]
[49]
[37]
[26]
[62]
[38]
[55]
[24]
[30]
[39]
[59]
[26]
[33]
[20]
[18]
[19]
[61]
[39]
[52]
[20]
[50]
[29]
[55]
[18]
[39]
[35]
[38]
[20]
[60]
[61]
[62]
[21]
[40]
[25]
[50]
[24]
[28]
[22]
[22]
[27]
[54]
[25]
[25]
[30]
[59]
[51]
[38]
[28]
[30]
[43]
[42]
[24]
[47]
[23]
[59]
[22]
[27]
[65]
[27]
[27]
[28]
[22]
[42]
[25]
[41]
[46]
[41]
[29]
[50]
[50]
[34]
[24]
[29]
[35]
[24]
[23]
[31]
[24]
[30]
[22]
[43]
[55]
[38]
[28]
[30]
[35]
[41]
[28]
[65]
[20]
[39]
[19]
[64]
[42]
[24]
[24]
[18]
[36]
[61]
[22]
[55]
[27]
[49]
[29]
[33]
[52]
[26]

{"sheet":"Trimmed Mean","index":22,"n_rows":40,"n_cols":11,"rows_kept":40,"cut":false,"hidden":false,"merged":[]}
["Remove 5% of smallest and largest values from data set. Then calculate the Mean"]
[null,null,null,"trim",0.05]
["No","Salaries",null,"n =",{"$f":"COUNT(B4:B40)","v":37}]
[1,1020,null,"# values to drop",{"$f":"E3*E2","v":1.85}]
[2,1030,null,"Round up to integer",{"$f":"ROUNDUP(E4,0)","v":2}]
[3,2150,null,"Method 1"]
[4,2861,null,"Trimmed Mean",{"$f":"AVERAGE(B6:B38)","v":3540.969696969697},null," =AVERAGE(B6:B38)"]
[5,2864]
[6,2964]
[7,2983,null,"Method 2"]
[8,3087,null,"Trimmed Mean",{"$f":"AVERAGE(OFFSET(B4,E5,,E3-E5*2))","v":3540.969696969697},null," =AVERAGE(OFFSET(B4,E5,,E3-E5*2))"]
[9,3167]
[10,3222,null,"TRIMMED MEAN built-in Excel function does not do the same thing:"]
[11,3234,null,"Method 3",null,null,null,null,null,{"$f":"E2&\"*\"&E3","v":"0.05*37"}]
[12,3267,null,"Trimmed Mean",{"$f":"TRIMMEAN(B4:B40,E2)","v":3636.2702702702704},null," =TRIMMEAN(B4:B40,E2)",null,null,{"$f":"E2*E3","v":1.85},"  == > Rounds down to 0,"]
[13,3275,null,null,null,null,null,null,null,null,"so no number excluded"]
[14,3283,null,"**the calculated percentage is rounded down to the nearest multiple of 2"]
[15,3334,null,"This measn in the example 1.85 gets rounded down to 0"]
[16,3336]
[17,3337,null,null,null,null,null,"No Values!!!!"]
[18,3440,null,{"$f":"AVERAGE(B5:B39)","v":3573.7714285714287},{"$f":"AVERAGE(B6:B38)","v":3540.969696969697},{"$f":"AVERAGE(B5:B38)","v":3467.1176470588234},{"$f":"AVERAGE(B6:B39)","v":3648.5882352941176},{"$f":"AVERAGE(B4:B40)","v":3636.2702702702704}]
[19,3442,null,"Note that, the specified percentage is the total percentage of values to be excluded. This percentage is divided by two, to get the number of values that are removed from each end of the range."]
[20,3480,null,"It should also be noted that, when Excel is calculating how many values to discard from the supplied array of values, the calculated percentage is rounded down to the nearest multiple of 2. For example, if you want to calculate the trimmed mean of an array of 10 values, then:"]
[21,3485,null,"A percentage of 15%, is 1.5 values, which will be rounded down to 0 (i.e. no values are discarded from the array before calculating the mean)"]
[22,3509,null,"A percentage of 20%, is 2 values, so 1 value will be discarded from each end of the range before calculating the mean of the remaining values"]
[23,3546,null,"A percentage of 25%, is 2.5 values, which will be rounded down to 2 (i.e. 1 value will be discarded from each end of the range before calculating the mean of the remaining values)"]
[24,3609]
[25,3626]
[26,3628]
[27,3633]
[28,3658]
[29,3796]
[30,3814]
[31,3875]
[32,3901]
[33,3946]
[34,5900]
[35,6200]
[36,7200]
[37,8440]

{"sheet":"Geometric Mean","index":23,"n_rows":69,"n_cols":12,"rows_kept":69,"cut":false,"hidden":false,"merged":[]}
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[null,null,null,null,null,null,null,null,"Growth Rate"," =","Growth Rate or Rate of Change for period","GR"]
[null,null,null,null,null,null,null,null,"Growth Factor"," =","Factor you can multiply by Beg to get End","GR + 1"]
[null,null,null,null,null,null,null,null,"n"," =","Number Of Total Periods","\"n\""]
[null,null,null,null,null,null,null,null,"Beg Value"," =","Value at beginning of first period","\"Beg\""]
[null,null,null,null,null,null,null,null,"End Value"," =","Value at end of last period","\"End\""]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[null,null,null,null,null,null,null,null,"FV","FV"]
["Returns for MSFT stock:",null,null,null,null,null,null,null,"PV","Beg"]
[null,null,null,null,null,null,null,null,"Period Rate","Geomean"]
["Date","Adj Close","% Change from Prev (Growth Rate)","Growth Rate + 1 = Growth Factor",null,"Start",{"$f":"B45","v":36.409999999999997},null,"NumberOfPeriods","n"]
["2015-01-02",46.23,null,null,null,"End",{"$f":"B32","v":46.23}]
["2014-12-01",46.45,null,null,null,"Mean"]
["2014-11-03",47.81,null,null,null,"Count, n"]
["2014-10-01",46.66,null,null,null,"Prove it!",null,{"$f":"IF(ISNUMBER(G35),\"Incorrect!\",\"\")","v":""},"FV formula = Beg*(1+rate)^n"]
["2014-09-02",46.07,null,null,null,null,null,"Arithmetic Mean overstates the return!"]
["2014-08-01",45.15,null,null,null,null,null,"Arithmetic Mean is for additive processes"]
["2014-07-01",42.62,null,null,null,null,null,"Geomean is for multiplicative processes"]
["2014-06-02",41.18,null,null,null,"(GM1 = X1*X2*…Xn)^(1/n)-1"]
["2014-05-01",40.43,null,null,null,"GeoMean, M1",null,null,"Growth Factor"]
["2014-04-01",39.619999999999997,null,null,null,"GeoMean, M1",null,null,"Growth Rate"]
["2014-03-03",40.200000000000003,null,null,null,"Count, n",null,null,"True \"Average\" Compounding Rate per Period"]
["2014-02-03",37.57,null,null,null,"Prove it!",null,{"$f":"IF(ISNUMBER(G43),\"Correct!\",\"\")","v":""},"FV formula = Beg*(1+rate)^n"]
["2014-01-02",36.83,null,null,null,null,null,null,"**Authors of our textbook do an admirable job of pointing this out, especially"]
["2013-12-02",36.409999999999997,null,null,null,"(End/Beg)^(1/n)-1",null,null,"given that some finance textbooks do not"]
[null,null,null,null,null,"Start",{"$f":"G31","v":36.409999999999997}]
[null,null,null,null,null,"End",{"$f":"G32","v":46.23}]
[null,null,null,null,null,"Average Compound Factor"]
[null,null,null,null,null,"Average Compound Rate",null,null,"GeoMean From Just Start and End and number of periods"]
[null,null,null,null,null,"Average Compound Rate"]
[]
[]
[null,null,null,null,null,"(GM1 = X1*X2*…Xn)^(1/n)-1"]
[null,null,null,null,null,"GeoMean, single Cell Formula",{"$f":"GEOMEAN(B32:B44/B33:B45)-1","v":1.8537844943960158E-2},null,"Must enter array formula with: Ctrl + Shift + Enter, then verify that curly brackets can be seen in formula bar."]
[]
[null,null,null,null,null,"Year","Investment"]
[null,null,null,null,null,1999,2500]
[null,null,null,null,null,2014,{"$f":"G57*(1+0.067)^(F58-F57)","v":6613.1184265048369}]
[]
[null,null,null,null,null,"Average Compounding Rate?",null,null,"(End/Beg)^(1/n)-1"]
[]
[]
[null,null,null,null,null,"Population Growth","Year","% Change + 1"]
[null,null,null,null,null,0.01,2004]
[null,null,null,null,null,5.0000000000000001E-3,2005]
[null,null,null,null,null,0.02,2006]
[null,null,null,null,null,2.5000000000000001E-2,2007]
[]
[null,null,null,null,null,"Geomertic Mean"]

{"sheet":"Geometric Mean (an)","index":24,"n_rows":69,"n_cols":12,"rows_kept":69,"cut":false,"hidden":false,"merged":[]}
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[null,null,null,null,null,null,null,null,"Growth Rate"," =","Growth Rate or Rate of Change for period","GR"]
[null,null,null,null,null,null,null,null,"Growth Factor"," =","Factor you can multiply by Beg to get End","GR + 1"]
[null,null,null,null,null,null,null,null,"n"," =","Number Of Total Periods","\"n\""]
[null,null,null,null,null,null,null,null,"Beg Value"," =","Value at beginning of first period","\"Beg\""]
[null,null,null,null,null,null,null,null,"End Value"," =","Value at end of last period","\"End\""]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[null,null,null,null,null,null,null,null,"FV","FV"]
["Returns for MSFT stock:",null,null,null,null,null,null,null,"PV","Beg"]
[null,null,null,null,null,null,null,null,"Period Rate","Geomean"]
["Date","Adj Close","% Change from Prev (Growth Rate)","Growth Rate + 1 = Growth Factor",null,"Start",{"$f":"B45","v":36.409999999999997},null,"NumberOfPeriods","n"]
["2015-01-02",46.23,{"$f":"B32/B33-1","v":-4.7362755651239041E-3},{"$f":"1+C32","v":0.9952637244348761},null,"End",{"$f":"B32","v":46.23}]
["2014-12-01",46.45,-2.8445931813428138E-2,{"$f":"1+C33","v":0.97155406818657186},null,"Mean",{"$f":"AVERAGE(C32:C44)","v":1.8859595090217122E-2}]
["2014-11-03",47.81,2.4646378054007823E-2,1.0246463780540078,null,"Count, n",{"$f":"COUNT(C32:C44)","v":13}]
["2014-10-01",46.66,1.2806598654221846E-2,1.0128065986542218,null,"Prove it!",{"$f":"G31*(1+G33)^G34","v":46.420209475578403},{"$f":"IF(ISNUMBER(G35),\"Incorrect!\",\"\")","v":"Incorrect!"},"FV formula = Beg*(1+rate)^n"]
["2014-09-02",46.07,2.0376522702104216E-2,1.0203765227021042,null,null,null,"Arithmetic Mean overstates the return!"]
["2014-08-01",45.15,5.9361801970905681E-2,1.0593618019709057,null,null,null,"Arithmetic Mean is for additive processes"]
["2014-07-01",42.62,3.4968431277319079E-2,1.0349684312773191,null,null,null,"Geomean is for multiplicative processes"]
["2014-06-02",41.18,1.855058125154585E-2,1.0185505812515459,null,"(GM1 = X1*X2*…Xn)^(1/n)-1"]
["2014-05-01",40.43,2.0444220090863219E-2,1.0204442200908632,null,"GeoMean, M1",{"$f":"GEOMEAN(D32:D44)","v":1.0185378449439602},null,"Growth Factor"]
["2014-04-01",39.619999999999997,-1.442786069651758E-2,0.98557213930348242,null,"GeoMean, M1",{"$f":"GEOMEAN(D32:D44)-1","v":1.8537844943960158E-2},null,"Growth Rate"]
["2014-03-03",40.200000000000003,7.0002661698163493E-2,1.0700026616981635,null,"Count, n",{"$f":"COUNT(C32:C44)","v":13},null,"True \"Average\" Compounding Rate per Period"]
["2014-02-03",37.57,2.0092316046701031E-2,1.020092316046701,null,"Prove it!",{"$f":"G31*(1+G41)^G42","v":46.23},{"$f":"IF(ISNUMBER(G43),\"Correct!\",\"\")","v":"Correct!"},"FV formula = Beg*(1+rate)^n"]
["2014-01-02",36.83,{"$f":"B44/B45-1","v":1.1535292502059979E-2},1.01153529250206,null,null,null,null,"**Authors of our textbook do an admirable job of pointing this out, especially"]
["2013-12-02",36.409999999999997,null,null,null,"(End/Beg)^(1/n)-1",null,null,"given that some finance textbooks do not"]
[null,null,null,null,null,"Start",{"$f":"G31","v":36.409999999999997}]
[null,null,null,null,null,"End",{"$f":"G32","v":46.23}]
[null,null,null,null,null,"Average Compound Factor",{"$f":"(G47/G46)^(1/G42)","v":1.0185378449439602}]
[null,null,null,null,null,"Average Compound Rate",{"$f":"(G47/G46)^(1/G42)-1","v":1.8537844943960158E-2},null,"GeoMean From Just Start and End and number of periods"]
[null,null,null,null,null,"Average Compound Rate",{"$f":"_xlfn.RRI(G42,G46,G47)","v":1.8537844943960158E-2}]
[]
[]
[null,null,null,null,null,"(GM1 = X1*X2*…Xn)^(1/n)-1"]
[null,null,null,null,null,"GeoMean, single Cell Formula",{"$f":"GEOMEAN(B32:B44/B33:B45)-1","v":1.8537844943960158E-2},null,"Must enter array formula with: Ctrl + Shift + Enter, then verify that curly brackets can be seen in formula bar."]
[]
[null,null,null,null,null,"Year","Investment"]
[null,null,null,null,null,1999,2500]
[null,null,null,null,null,2014,{"$f":"G57*(1+0.067)^(F58-F57)","v":6613.1184265048369}]
[]
[null,null,null,null,null,"Average Compounding Rate?",{"$f":"_xlfn.RRI(F58-F57,G57,G58)","v":6.6999999999999948E-2},null,"(End/Beg)^(1/n)-1"]
[]
[]
[null,null,null,null,null,"Population Growth","Year","% Change + 1"]
[null,null,null,null,null,0.01,2004,{"$f":"1+F64","v":1.01}]
[null,null,null,null,null,5.0000000000000001E-3,2005,{"$f":"1+F65","v":1.0049999999999999}]
[null,null,null,null,null,0.02,2006,1.02]
[null,null,null,null,null,2.5000000000000001E-2,2007,1.0249999999999999]
[]
[null,null,null,null,null,"Geomertic Mean",{"$f":"GEOMEAN(H64:H67)-1","v":1.4969211019458584E-2}]

{"sheet":"PercentiileAndQuartile","index":25,"n_rows":619,"n_cols":13,"rows_kept":619,"cut":false,"hidden":false,"merged":[]}
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
["Number","Name","Entrance Exam Scores",null,null,null,null,{"$f":"14/18","v":0.77777777777777779}]
[1,"Jo",12,null,"Company Policy with percentile",0.75]
[2,"Don",54,null,"Count, n",null,null,"Percentiles"]
[3,"Bob",56,null,"Position in List",null,null,"Formula for position = P%*(n+1)"]
[4,"Minny",62,null,{"$f":"\"How far between \"&INDEX(A22:A39,MATCH(_xlfn.PERCENTILE.EXC(C22:C39,F22),C22:C39))&\" & \"&INDEX(A22:A39,MATCH(_xlfn.PERCENTILE.EXC(C22:C39,F22),C22:C39))+1&\"?\"","v":"How far between 14 & 15?"},null,null,{"$f":"IF(ISNUMBER(F25),\"<<== \"&TEXT(F25,\"0.00%\")&\" of the way through the interval\",\"\")","v":""}]
[5,"Gigi",69,null,{"$f":"\"Difference between \"&INDEX(A22:A39,MATCH(_xlfn.PERCENTILE.EXC(C22:C39,F22),C22:C39))&\" & \"&INDEX(A22:A39,MATCH(_xlfn.PERCENTILE.EXC(C22:C39,F22),C22:C39))+1","v":"Difference between 14 & 15"}]
[6,"Lim",72,null,{"$f":"\"How far from \"&INDEX(A22:A39,MATCH(_xlfn.PERCENTILE.EXC(C22:C39,F22),C22:C39))&\"?\"","v":"How far from 14?"},null,null,{"$f":"IF(ISNUMBER(F27),\"<<== Actually how far through interval\",\"\")","v":""}]
[7,"Chris",73,null,"Percentile Marker",null,null,{"$f":"IF(ISNUMBER(F28),\"<<== \"&F28&\" marks the point at which approx. \",\"\")","v":""}]
[8,"Shelia",74,null,"Percentile Marker with PERCENTILE.EXC",null,null,{"$f":"IF(ISNUMBER(F28),\"        approx. \"&TEXT(F22,\"0.00%\")&\" of the values are below  and\",\"\")","v":""}]
[9,"Tommy",78,null,null,null,null,{"$f":"IF(ISNUMBER(F28),\"        approx. \"&TEXT(1-F22,\"0.00%\")&\" of the values are above.\",\"\")","v":""}]
[10,"Fran",82,null,"Company Policy with quartile",3]
[11,"Homer",83,null,"Quartile Marker with QUARTILE.EXC"]
[12,"Tina",87]
[13,"Sue",88,null,"Your Score",89]
[14,"Sioux",88,null,"Percentile rank with PERCENTRANK.EXC"]
[15,"Chin",91,null,{"$f":"IF(ISNUMBER(F35),\"Reverse: you give it value and it calculates percentile\",\"\")","v":""}]
[16,"Your Name",92]
[17,"Tyrone",95]
[18,"Karen",100]
[]
["Number","Name","Entrance Exam Scores"]
[1,"Jo",12,null,"Company Policy with percentile",0.5,{"$f":"9/18","v":0.5}]
[2,"Don",54,null,"Percentile Marker with PERCENTILE.EXC",null,null,{"$f":"IF(ISNUMBER(F43),\"<<== \"&F43&\" marks the point at which approx. \",\"\")","v":""}]
[3,"Bob",56,null,"Median = 50% Percentile Marker = \"in the middle\"",null,null,{"$f":"IF(ISNUMBER(F43),\"        approx. \"&TEXT(F42,\"0.00%\")&\" of the values are below  and\",\"\")","v":""}]
[4,"Minny",62,null,"Average Middle Two",null,null,{"$f":"IF(ISNUMBER(F43),\"        approx. \"&TEXT(1-F42,\"0.00%\")&\" of the values are above.\",\"\")","v":""}]
[5,"Gigi",69]
[6,"Lim",72,null,null,null,null,"Quartile"]
[7,"Chris",73,null,"Quartile 1",null,null,1]
[8,"Shelia",74,null,"Quartile 2",null,null,2]
[9,"Tommy",78,null,"Quartile 3",null,null,3]
[10,"Fran",82]
[11,"Homer",83]
[12,"Tina",87]
[13,"Sue",88]
[14,"Sioux",88]
[15,"Chin",91]
[16,"Your Name",92]
[17,"Tyrone",95]
[18,"Karen",100]
[]
["Number","Name","Entrance Exam Scores",null,"Quintiles (5 parts with 4 markers):",null,null,"P%"]
[1,"Jo",12,null,"Marker 1",null,null,0.2]
[2,"Don",54,null,"Marker 2",null,null,0.4]
[3,"Bob",56,null,"Marker 3",null,null,0.6]
[4,"Minny",62,null,"Marker 4",null,null,0.8]
[5,"Gigi",69]
[6,"Lim",72,null,"Deciles (10 parts with 9 markers",null,null,"P%"]
[7,"Chris",73,null,"Marker 1",null,null,0.1]
[8,"Shelia",74,null,"Marker 2",null,null,0.2]
[9,"Tommy",78,null,"Marker 3",null,null,0.3]
[10,"Fran",82,null,"Marker 4",null,null,0.4]
[11,"Homer",83,null,"Marker 5",null,null,0.5]
[12,"Tina",87,null,"Marker 6",null,null,0.6]
[13,"Sue",88,null,"Marker 7",null,null,0.7]
[14,"Sioux",88,null,"Marker 8",null,null,0.8]
[15,"Chin",91,null,"Marker 9",null,null,0.9]
[16,"Your Name",92]
[17,"Tyrone",95]
[18,"Karen",100]
[]
["Number","Name","Entrance Exam Scores",null,"Percentile Rank"]
[1,"Jo",12,null,{"$f":"_xlfn.PERCENTRANK.EXC($C$82:$C$99,C82)","v":5.1999999999999998E-2}]
[2,"Don",54,null,{"$f":"_xlfn.PERCENTRANK.EXC($C$82:$C$99,C83)","v":0.105}]
[3,"Bob",56,null,0.157]
[4,"Minny",62,null,0.21]
[5,"Gigi",69,null,0.26300000000000001]
[6,"Lim",72,null,0.315]
[7,"Chris",73,null,0.36799999999999999]
[8,"Shelia",74,null,0.42099999999999999]
[9,"Tommy",78,null,0.47299999999999998]
[10,"Fran",82,null,0.52600000000000002]
[11,"Homer",83,null,0.57799999999999996]
[12,"Tina",87,null,0.63100000000000001]
[13,"Sue",88,null,0.68400000000000005]
[14,"Sioux",88,null,0.68400000000000005]
[15,"Chin",91,null,0.78900000000000003]
[16,"Your Name",92,null,0.84199999999999997]
[17,"Tyrone",95,null,0.89400000000000002]
[18,"Karen",100,null,0.94699999999999995]
[]
[]
[]
["Number","Name","Entrance Exam Scores"]
[1,"Jo",12,null,"Percentile",0.75,null,"Video about another method for calculating percentile:"]
[2,"Don",54,null,"PERCENTILE.EXC, exclude 1 and 0, where 1 = max and 0 = min",null,null,"Excel 2010 Statistics #27: Excel 2010 Functions for Percentile, Quartile and Rank "]
[3,"Bob",56,null,"PERCENTILE.INC, include 1 and 0, where 1 = max and 0 = min",null,null,"https://www.youtube.com/watch?v=XllEMSjVGGk"]
[4,"Minny",62,null,"**Algorithm for PERCENTILE.INC: Position = p*n+(1-p)"]
[5,"Gigi",69,null,null,"PERCENTILE.INC",null,null,"PERCENTILE.EXC",null,null,"PERCENTILE.INC"]
[6,"Lim",72,null,"Inclusive Example:","Marker","P%",null,"Marker","P%",null,"Long Hand Formula:"]
[7,"Chris",73,null,null,null,0,null,null,0,null,{"$f":"F104*COUNT($C$104:$C$121)+(1-F104)","v":13.75}," <<== Position"]
[8,"Shelia",74,null,null,null,0.25,null,null,0.25,null,{"$f":"C116+(C117-C116)*MOD(L110,1)","v":88}," <<== Marker"]
[9,"Tommy",78,null,null,null,0.5,null,null,0.5]
[10,"Fran",82,null,null,null,0.75,null,null,0.75]
[11,"Homer",83,null,null,null,1,null,null,1]
[12,"Tina",87]
[13,"Sue",88,null,null,"Marker","Quartile"]
[14,"Sioux",88,null,null,null,0]
[15,"Chin",91,null,null,null,1]
[16,"Your Name",92,null,null,null,2]
[17,"Tyrone",95,null,null,null,3]
[18,"Karen",100,null,null,null,4]
[]
[]
["Entrance Exam Scores",null,null,null,"Function","%","Result"]
[32,null,null,null,"PERCENTILE.EXC",0.75]
[43,null,null,null,"PERCENTILE.INC",0.75]
[86,null,null,null,"QUARTILE.EXC",3]
[63,null,null,null,"QUARTILE.INC",3]
[36,null,null,null,"PERCENTRANK.EXC",74]
[36,null,null,null,"PERCENTRANK.INC",74]
[52]
[88]
[51]
[89]
[44]
[39]
[10]
[74]
[82]
[20]
[14]
[43]
[10]
[41]
[44]
[86]
[89]
[78]
[30]
[8]
[13]
[4]
[38]
[83]
[70]
[37]
[69]
[86]
[63]
[23]
[20]
[23]
[61]
[60]
[93]
[75]
[29]
[11]
[29]
[2]
[25]
[94]
[73]
[56]
[6]
[33]
[27]
[64]
[5]
[72]
[87]
[2]
[33]
[48]
[12]
[57]
[42]
[41]
[89]
[63]
[26]
[78]
[86]
[60]
[34]
[7]
[39]
[10]
[94]
[47]
[71]
[29]
[16]
[72]
[45]
[1]
[10]
[81]
[51]
[78]
[20]
[32]
[60]
[82]
[64]
[77]
[68]
[43]
[8]
[83]
[15]
[79]
[65]
[74]
[2]
[78]
[59]
[79]
[91]
[83]
[62]
[61]
[59]
[45]
[62]
[27]
[65]
[82]
[48]
[70]
[91]
[71]
[92]
[71]
[56]
[60]
[71]
[96]
[66]
[55]
[69]
[78]
[12]
[57]
[71]
[75]
[45]
[69]
[68]
[36]
[67]
[69]
[70]
[69]
[24]
[13]
[88]
[68]
[96]
[9]
[85]
[69]
[18]
[72]
[74]
[91]
[74]
[93]
[83]
[80]
[87]
[78]
[61]
[71]
[51]
[69]
[36]
[73]
[71]
[73]
[74]
[41]
[40]
[86]
[74]
[92]
[52]
[76]
[64]
[85]
[96]
[81]
[75]
[73]
[66]
[79]
[65]
[78]
[77]
[76]
[44]
[52]
[65]
[65]
[74]
[36]
[37]
[62]
[38]
[76]
[52]
[65]
[38]
[43]
[78]
[72]
[63]
[57]
[86]
[4]
[77]
[77]
[59]
[70]
[61]
[81]
[94]
[84]
[69]
[78]
[2]
[69]
[80]
[83]
[68]
[68]
[76]
[73]
[38]
[28]
[97]
[50]
[48]
[66]
[66]
[10]
[72]
[100]
[73]
[68]
[71]
[73]
[85]
[75]
[82]
[73]
[67]
[37]
[79]
[9]
[73]
[59]
[18]
[32]
[75]
[79]
[87]
[41]
[69]
[65]
[81]
[44]
[1]
[70]
[45]
[68]
[67]
[73]
[56]
[46]
[76]
[70]
[35]
[65]
[71]
[94]
[3]
[9]
[75]
[51]
[2]
[71]
[51]
[81]
[66]
[77]
[43]
[68]
[77]
[74]
[4]
[80]
[39]
[69]
[73]
[66]
[69]
[61]
[62]
[33]
[36]
[89]
[63]
[70]
[28]
[54]
[48]
[71]
[82]
[64]
[58]
[27]
[74]
[71]
[81]
[62]
[68]
[16]
[72]
[52]
[51]
[60]
[69]
[46]
[79]
[88]
[72]
[63]
[14]
[35]
[72]
[71]
[73]
[76]
[72]
[93]
[10]
[73]
[45]
[58]
[43]
[19]
[45]
[74]
[49]
[38]
[45]
[77]
[66]
[75]
[79]
[50]
[93]
[64]
[82]
[87]
[73]
[15]
[13]
[58]
[79]
[82]
[36]
[75]
[31]
[72]
[39]
[74]
[15]
[65]
[41]
[77]
[87]
[75]
[86]
[77]
[75]
[75]
[70]
[72]
[59]
[16]
[36]
[37]
[65]
[44]
[56]
[79]
[64]
[74]
[70]
[27]
[23]
[20]
[72]
[48]
[65]
[63]
[90]
[58]
[49]
[78]
[72]
[71]
[70]
[62]
[78]
[51]
[81]
[36]
[63]
[74]
[38]
[71]
[41]
[56]
[72]
[92]
[86]
[83]
[87]
[82]
[86]
[67]
[71]
[75]
[57]
[61]
[81]
[47]
[72]
[9]
[37]
[99]
[57]
[76]
[72]
[73]
[72]
[84]
[56]
[87]
[76]
[81]
[48]
[59]
[88]
[82]
[26]
[80]
[67]
[77]
[38]
[47]
[88]
[11]
[36]
[7]
[80]
[68]
[82]
[37]
[58]
[71]
[54]
[31]
[50]
[73]
[97]
[49]
[38]
[50]
[40]
[41]
[48]
[80]
[43]
[16]
[76]
[77]
[65]
[75]
[66]
[73]
[26]
[70]
[75]
[78]
[70]
[88]
[67]
[87]
[73]
[76]
[]
[]
[]
["Rand Formula:"]
[{"$f":"CHOOSE(RANDBETWEEN(1,3),ROUND(_xlfn.NORM.INV(RAND(),73,5),0),RANDBETWEEN(35,88),RANDBETWEEN(0,100))","v":69}]

{"sheet":"PercentiileAndQuartile (an)","index":26,"n_rows":619,"n_cols":13,"rows_kept":619,"cut":false,"hidden":false,"merged":[]}
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[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
["Number","Name","Entrance Exam Scores"]
[1,"Jo",12,null,"Company Policy with percentile",0.75,{"$f":"14/18","v":0.77777777777777779}]
[2,"Don",54,null,"Count, n",{"$f":"COUNT(C22:C39)","v":18},null,"Percentiles"]
[3,"Bob",56,null,"Position in List",{"$f":"F22*(F23+1)","v":14.25},null,"Formula for position = P%*(n+1)"]
[4,"Minny",62,null,{"$f":"\"How far between \"&INDEX(A22:A39,MATCH(_xlfn.PERCENTILE.EXC(C22:C39,F22),C22:C39))&\" & \"&INDEX(A22:A39,MATCH(_xlfn.PERCENTILE.EXC(C22:C39,F22),C22:C39))+1&\"?\"","v":"How far between 14 & 15?"},{"$f":"MOD(F24,1)","v":0.25},null,{"$f":"IF(ISNUMBER(F25),\"<<== \"&TEXT(F25,\"0.00%\")&\" of the way through the interval\",\"\")","v":"<<== 25.00% of the way through the interval"}]
[5,"Gigi",69,null,{"$f":"\"Difference between \"&INDEX(A22:A39,MATCH(_xlfn.PERCENTILE.EXC(C22:C39,F22),C22:C39))&\" & \"&INDEX(A22:A39,MATCH(_xlfn.PERCENTILE.EXC(C22:C39,F22),C22:C39))+1","v":"Difference between 14 & 15"},{"$f":"C36-C35","v":3}]
[6,"Lim",72,null,{"$f":"\"How far from \"&INDEX(A22:A39,MATCH(_xlfn.PERCENTILE.EXC(C22:C39,F22),C22:C39))&\"?\"","v":"How far from 14?"},{"$f":"F26*F25","v":0.75},null,{"$f":"IF(ISNUMBER(F27),\"<<== Actually how far through interval\",\"\")","v":"<<== Actually how far through interval"}]
[7,"Chris",73,null,"Percentile Marker",{"$f":"C35+F27","v":88.75},null,{"$f":"IF(ISNUMBER(F28),\"<<== \"&F28&\" marks the point at which approx. \",\"\")","v":"<<== 88.75 marks the point at which approx. "}]
[8,"Shelia",74,null,"Percentile Marker with PERCENTILE.EXC",{"$f":"_xlfn.PERCENTILE.EXC(C22:C39,F22)","v":88.75},null,{"$f":"IF(ISNUMBER(F28),\"        approx. \"&TEXT(F22,\"0.00%\")&\" of the values are below  and\",\"\")","v":"        approx. 75.00% of the values are below  and"}]
[9,"Tommy",78,null,null,null,null,{"$f":"IF(ISNUMBER(F28),\"        approx. \"&TEXT(1-F22,\"0.00%\")&\" of the values are above.\",\"\")","v":"        approx. 25.00% of the values are above."}]
[10,"Fran",82,null,"Company Policy with quartile",3]
[11,"Homer",83,null,"Quartile Marker with QUARTILE.EXC",{"$f":"_xlfn.QUARTILE.EXC(C22:C39,F31)","v":88.75}]
[12,"Tina",87]
[13,"Sue",88,null,"Your Score",89]
[14,"Sioux",88,null,"Percentile rank with PERCENTRANK.EXC",{"$f":"_xlfn.PERCENTRANK.EXC(C22:C39,F34)","v":0.754}]
[15,"Chin",91,null,{"$f":"IF(ISNUMBER(F35),\"Reverse: you give it value and it calculates percentile\",\"\")","v":"Reverse: you give it value and it calculates percentile"}]
[16,"Your Name",92]
[17,"Tyrone",95]
[18,"Karen",100]
[]
["Number","Name","Entrance Exam Scores"]
[1,"Jo",12,null,"Company Policy with percentile",0.5,{"$f":"9/18","v":0.5}]
[2,"Don",54,null,"Percentile Marker with PERCENTILE.EXC",{"$f":"_xlfn.PERCENTILE.EXC(C42:C59,F42)","v":80},null,{"$f":"IF(ISNUMBER(F43),\"<<== \"&F43&\" marks the point at which approx. \",\"\")","v":"<<== 80 marks the point at which approx. "}]
[3,"Bob",56,null,"Median = 50% Percentile Marker = \"in the middle\"",{"$f":"MEDIAN(C42:C59)","v":80},null,{"$f":"IF(ISNUMBER(F43),\"        approx. \"&TEXT(F42,\"0.00%\")&\" of the values are below  and\",\"\")","v":"        approx. 50.00% of the values are below  and"}]
[4,"Minny",62,null,"Average Middle Two",{"$f":"AVERAGE(C50:C51)","v":80},null,{"$f":"IF(ISNUMBER(F43),\"        approx. \"&TEXT(1-F42,\"0.00%\")&\" of the values are above.\",\"\")","v":"        approx. 50.00% of the values are above."}]
[5,"Gigi",69]
[6,"Lim",72,null,null,null,null,"Quartile"]
[7,"Chris",73,null,"Quartile 1",{"$f":"_xlfn.QUARTILE.EXC($C$42:$C$59,H48)","v":67.25},null,1]
[8,"Shelia",74,null,"Quartile 2",{"$f":"_xlfn.QUARTILE.EXC($C$42:$C$59,H49)","v":80},null,2]
[9,"Tommy",78,null,"Quartile 3",88.75,null,3]
[10,"Fran",82]
[11,"Homer",83]
[12,"Tina",87]
[13,"Sue",88]
[14,"Sioux",88]
[15,"Chin",91]
[16,"Your Name",92]
[17,"Tyrone",95]
[18,"Karen",100]
[]
["Number","Name","Entrance Exam Scores",null,"Quintiles (5 parts with 4 markers):",null,null,"P%"]
[1,"Jo",12,null,"Marker 1",{"$f":"_xlfn.PERCENTILE.EXC($C$62:$C$79,H62)","v":60.800000000000004},null,0.2]
[2,"Don",54,null,"Marker 2",{"$f":"_xlfn.PERCENTILE.EXC($C$62:$C$79,H63)","v":73.599999999999994},null,0.4]
[3,"Bob",56,null,"Marker 3",84.6,null,0.6]
[4,"Minny",62,null,"Marker 4",91.2,null,0.8]
[5,"Gigi",69]
[6,"Lim",72,null,"Deciles (10 parts with 9 markers",null,null,"P%"]
[7,"Chris",73,null,"Marker 1",{"$f":"_xlfn.PERCENTILE.EXC($C$62:$C$79,H68)","v":49.800000000000004},null,0.1]
[8,"Shelia",74,null,"Marker 2",{"$f":"_xlfn.PERCENTILE.EXC($C$62:$C$79,H69)","v":60.800000000000004},null,0.2]
[9,"Tommy",78,null,"Marker 3",71.099999999999994,null,0.3]
[10,"Fran",82,null,"Marker 4",73.599999999999994,null,0.4]
[11,"Homer",83,null,"Marker 5",80,null,0.5]
[12,"Tina",87,null,"Marker 6",84.6,null,0.6]
[13,"Sue",88,null,"Marker 7",88,null,0.7]
[14,"Sioux",88,null,"Marker 8",91.2,null,0.8]
[15,"Chin",91,null,"Marker 9",95.5,null,0.9]
[16,"Your Name",92]
[17,"Tyrone",95]
[18,"Karen",100]
[]
["Number","Name","Entrance Exam Scores",null,"Percentile Rank"]
[1,"Jo",12,null,{"$f":"_xlfn.PERCENTRANK.EXC($C$82:$C$99,C82)","v":5.1999999999999998E-2}]
[2,"Don",54,null,{"$f":"_xlfn.PERCENTRANK.EXC($C$82:$C$99,C83)","v":0.105}]
[3,"Bob",56,null,0.157]
[4,"Minny",62,null,0.21]
[5,"Gigi",69,null,0.26300000000000001]
[6,"Lim",72,null,0.315]
[7,"Chris",73,null,0.36799999999999999]
[8,"Shelia",74,null,0.42099999999999999]
[9,"Tommy",78,null,0.47299999999999998]
[10,"Fran",82,null,0.52600000000000002]
[11,"Homer",83,null,0.57799999999999996]
[12,"Tina",87,null,0.63100000000000001]
[13,"Sue",88,null,0.68400000000000005]
[14,"Sioux",88,null,0.68400000000000005]
[15,"Chin",91,null,0.78900000000000003]
[16,"Your Name",92,null,0.84199999999999997]
[17,"Tyrone",95,null,0.89400000000000002]
[18,"Karen",100,null,0.94699999999999995]
[]
[]
[]
["Number","Name","Entrance Exam Scores"]
[1,"Jo",12,null,"Percentile",0.75,null,"Video about another method for calculating percentile:"]
[2,"Don",54,null,"PERCENTILE.EXC, exclude 1 and 0, where 1 = max and 0 = min",{"$f":"_xlfn.PERCENTILE.EXC(C104:C121,F104)","v":88.75},null,"Excel 2010 Statistics #27: Excel 2010 Functions for Percentile, Quartile and Rank "]
[3,"Bob",56,null,"PERCENTILE.INC, include 1 and 0, where 1 = max and 0 = min",{"$f":"_xlfn.PERCENTILE.INC(C104:C121,F104)","v":88},null,"https://www.youtube.com/watch?v=XllEMSjVGGk"]
[4,"Minny",62,null,"**Algorithm for PERCENTILE.INC: Position = p*n+(1-p)"]
[5,"Gigi",69,null,null,"PERCENTILE.INC",null,null,"PERCENTILE.EXC",null,null,"PERCENTILE.INC"]
[6,"Lim",72,null,"Inclusive Example:","Marker","P%",null,"Marker","P%",null,"Long Hand Formula:"]
[7,"Chris",73,null,null,{"$f":"_xlfn.PERCENTILE.INC($C$104:$C$121,G110)","v":12},0,null,{"$f":"_xlfn.PERCENTILE.EXC($C$104:$C$121,J110)","v":{"$error":"#NUM!"}},0,null,{"$f":"F104*COUNT($C$104:$C$121)+(1-F104)","v":13.75}," <<== Position"]
[8,"Shelia",74,null,null,{"$f":"_xlfn.PERCENTILE.INC($C$104:$C$121,G111)","v":69.75},0.25,null,{"$f":"_xlfn.PERCENTILE.EXC($C$104:$C$121,J111)","v":67.25},0.25,null,{"$f":"C116+(C117-C116)*MOD(L110,1)","v":88}," <<== Marker"]
[9,"Tommy",78,null,null,80,0.5,null,80,0.5]
[10,"Fran",82,null,null,88,0.75,null,88.75,0.75]
[11,"Homer",83,null,null,100,1,null,{"$error":"#NUM!"},1]
[12,"Tina",87]
[13,"Sue",88,null,null,"Marker","Quartile"]
[14,"Sioux",88,null,null,{"$f":"_xlfn.QUARTILE.INC($C$104:$C$121,G117)","v":12},0]
[15,"Chin",91,null,null,{"$f":"_xlfn.QUARTILE.INC($C$104:$C$121,G118)","v":69.75},1]
[16,"Your Name",92,null,null,80,2]
[17,"Tyrone",95,null,null,88,3]
[18,"Karen",100,null,null,100,4]
[]
[]
["Entrance Exam Scores",null,null,null,"Function","%","Result"]
[32,null,null,null,"PERCENTILE.EXC",0.75,{"$f":"_xlfn.PERCENTILE.EXC($A$125:$A$614,F125)","v":76}]
[43,null,null,null,"PERCENTILE.INC",0.75,{"$f":"_xlfn.PERCENTILE.INC($A$125:$A$614,F126)","v":76}]
[86,null,null,null,"QUARTILE.EXC",3,{"$f":"_xlfn.QUARTILE.EXC($A$125:$A$614,F127)","v":76}]
[63,null,null,null,"QUARTILE.INC",3,{"$f":"_xlfn.QUARTILE.INC($A$125:$A$614,F128)","v":76}]
[36,null,null,null,"PERCENTRANK.EXC",74,{"$f":"_xlfn.PERCENTRANK.EXC($A$125:$A$614,F129)","v":0.68600000000000005}]
[36,null,null,null,"PERCENTRANK.INC",74,{"$f":"_xlfn.PERCENTRANK.INC($A$125:$A$614,F130)","v":0.68700000000000006}]
[52]
[88]
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[14]
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[2]
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[50]
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[10]
[72]
[100]
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[73]
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[79]
[9]
[73]
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[75]
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[1]
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[19]
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[77]
[66]
[75]
[79]
[50]
[93]
[64]
[82]
[87]
[73]
[15]
[13]
[58]
[79]
[82]
[36]
[75]
[31]
[72]
[39]
[74]
[15]
[65]
[41]
[77]
[87]
[75]
[86]
[77]
[75]
[75]
[70]
[72]
[59]
[16]
[36]
[37]
[65]
[44]
[56]
[79]
[64]
[74]
[70]
[27]
[23]
[20]
[72]
[48]
[65]
[63]
[90]
[58]
[49]
[78]
[72]
[71]
[70]
[62]
[78]
[51]
[81]
[36]
[63]
[74]
[38]
[71]
[41]
[56]
[72]
[92]
[86]
[83]
[87]
[82]
[86]
[67]
[71]
[75]
[57]
[61]
[81]
[47]
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[9]
[37]
[99]
[57]
[76]
[72]
[73]
[72]
[84]
[56]
[87]
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[81]
[48]
[59]
[88]
[82]
[26]
[80]
[67]
[77]
[38]
[47]
[88]
[11]
[36]
[7]
[80]
[68]
[82]
[37]
[58]
[71]
[54]
[31]
[50]
[73]
[97]
[49]
[38]
[50]
[40]
[41]
[48]
[80]
[43]
[16]
[76]
[77]
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[26]
[70]
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[73]
[76]
[]
[]
[]
["Rand Formula:"]
[{"$f":"CHOOSE(RANDBETWEEN(1,3),ROUND(_xlfn.NORM.INV(RAND(),73,5),0),RANDBETWEEN(35,88),RANDBETWEEN(0,100))","v":43}]

{"sheet":"Rank","index":27,"n_rows":15,"n_cols":6,"rows_kept":15,"cut":false,"hidden":false,"merged":[]}
["Sales Reps","Sales","RANK.EQ","Percentile Rank",null,"RANK.AVE"]
["Sioux",57635]
["Chris",77866]
["Sue",72926]
["Phil",61073]
["Herb",76079]
["Tina",76363]
["Luong",72097]
["Fred",56871]
["Chin",74487]
["Choi",{"$f":"B10","v":74487}]
[]
["RANK.EQ deals with ties as equal, RANK.AVE deals with ties by averaging the positional values; ex: (4+5)/2 = 4.5"]
["RANK.EQ or RANK.AVE function:\nref is all the values,\nnumber is the particular value to rank,\n0 = big to small (order)"]
["Screen shot of Rank & Percentile Excel tool:"]

{"sheet":"Stock","index":28,"n_rows":71,"n_cols":5,"rows_kept":71,"cut":false,"hidden":true,"merged":[]}
["Based on Average Returns, which stock do you prefer?"]
[]
["Year","Stock Returns A","Stock Returns B"]
[2001,0.05,0.02]
[{"$f":"A4+1","v":2002},0.1,0.04]
[2003,0.12,3.5000000000000003E-2]
[2004,0.17,5.5E-2]
[2005,0.19,0.04]
[2006,0.01,4.2000000000000003E-2]
[2007,-0.15,4.2999999999999997E-2]
[2008,-0.03,4.7E-2]
[2009,0.124,0.05]
[2010,5.5E-2,5.0999999999999997E-2]
[2011,0.1,0.03]
[2012,6.5000000000000002E-2,2.9000000000000001E-2]
[2013,-0.02,4.5999999999999999E-2]
[2014,-0.22,4.9000000000000002E-2]
[2015,0.15,4.1000000000000002E-2]
["GeoMean",{"$f":"GEOMEAN(B4:B18+1)-1","v":4.1169961128548316E-2},{"$f":"GEOMEAN(C4:C18+1)-1","v":4.1159802560870595E-2}]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
["X",{"$f":"B3","v":"Stock Returns A"},null,"X",{"$f":"C3","v":"Stock Returns B"}]
[{"$f":"B4","v":0.05},0.2,null,{"$f":"C4","v":0.02},0.7]
[0.1,0.2,null,0.04,0.7]
[0.12,0.2,null,3.5000000000000003E-2,0.7]
[0.17,0.2,null,5.5E-2,0.7]
[0.19,0.2,null,0.04,0.7]
[0.01,0.2,null,4.2000000000000003E-2,0.7]
[-0.15,0.2,null,4.2999999999999997E-2,0.7]
[-0.03,0.2,null,4.7E-2,0.7]
[0.124,0.2,null,0.05,0.7]
[5.5E-2,0.2,null,5.0999999999999997E-2,0.7]
[0.1,0.2,null,0.03,0.7]
[6.5000000000000002E-2,0.2,null,2.9000000000000001E-2,0.7]
[-0.02,0.2,null,4.5999999999999999E-2,0.7]
[-0.22,0.2,null,4.9000000000000002E-2,0.7]
[0.15,0.2,null,4.1000000000000002E-2,0.7]
[]
["X","Average Stock A",null,"X","Average Stock B"]
[{"$f":"B19","v":4.1169961128548316E-2},0.35,null,{"$f":"C19","v":4.1159802560870595E-2},0.9]
[]
[]
[]
["Lower","Upper","Return Bins","RF Stock A","RF Stock B"]
[-0.25,{"$f":"A60+0.05","v":-0.2},{"$f":"TEXT(A60,\"0%\")&CHAR(10)&\"up to\"&CHAR(10)&TEXT(B60,\"0%\")","v":"-25%\nup to\n-20%"},{"$f":"COUNTIFS(B$4:B$18,\">=\"&$A60,B$4:B$18,\"<\"&$B60)/$B$71","v":6.6666666666666666E-2},{"$f":"COUNTIFS(C$4:C$18,\">=\"&$A60,C$4:C$18,\"<\"&$B60)/$B$71","v":0}]
[{"$f":"B60","v":-0.2},-0.15000000000000002,{"$f":"TEXT(A61,\"0%\")&CHAR(10)&\"up to\"&CHAR(10)&TEXT(B61,\"0%\")","v":"-20%\nup to\n-15%"},0,0]
[-0.15000000000000002,-0.10000000000000002,"-15%\nup to\n-10%",6.6666666666666666E-2,0]
[-0.10000000000000002,-5.0000000000000017E-2,"-10%\nup to\n-5%",0,0]
[-5.0000000000000017E-2,0,"-5%\nup to\n0%",0.13333333333333333,0]
[0,0.05,"0%\nup to\n5%",6.6666666666666666E-2,0.8]
[0.05,0.1,"5%\nup to\n10%",0.2,0.2]
[0.1,0.15000000000000002,"10%\nup to\n15%",0.26666666666666666,0]
[0.15000000000000002,0.2,"15%\nup to\n20%",0.2,0]
[0.2,0.25,"20%\nup to\n25%",0,0]
[]
["Count",{"$f":"COUNT(A4:A18)","v":15}]

{"sheet":"Rank (an)","index":29,"n_rows":15,"n_cols":12,"rows_kept":15,"cut":false,"hidden":false,"merged":[]}
["Sales Reps","Sales","RANK.EQ","Percentile Rank",null,"RANK.AVE",null,null,"Point","Sales","Rank","Percent"]
["Sioux",57635,{"$f":"_xlfn.RANK.EQ(B2,$B$2:$B$11,0)","v":9},{"$f":"_xlfn.PERCENTRANK.INC($B$2:$B$11,B2)","v":0.111},null,{"$f":"_xlfn.RANK.AVG(B2,$B$2:$B$11,0)","v":9},null,null,2,77866,1,1]
["Chris",77866,{"$f":"_xlfn.RANK.EQ(B3,$B$2:$B$11,0)","v":1},{"$f":"_xlfn.PERCENTRANK.INC($B$2:$B$11,B3)","v":1},null,{"$f":"_xlfn.RANK.AVG(B3,$B$2:$B$11,0)","v":1},null,null,6,76363,2,0.88800000000000001]
["Sue",72926,6,0.44400000000000001,null,6,null,null,5,76079,3,0.77700000000000002]
["Phil",61073,8,0.222,null,8,null,null,9,74487,4,0.55500000000000005]
["Herb",76079,3,0.77700000000000002,null,3,null,null,10,74487,4,0.55500000000000005]
["Tina",76363,2,0.88800000000000001,null,2,null,null,3,72926,6,0.44400000000000001]
["Luong",72097,7,0.33300000000000002,null,7,null,null,7,72097,7,0.33300000000000002]
["Fred",56871,10,0,null,10,null,null,4,61073,8,0.222]
["Chin",74487,4,0.55500000000000005,null,4.5,null,null,1,57635,9,0.111]
["Choi",{"$f":"B10","v":74487},4,0.55500000000000005,null,4.5,null,null,8,56871,10,0]
[]
["RANK.EQ deals with ties as equal, RANK.AVE deals with ties by averaging the positional values; ex: (4+5)/2 = 4.5"]
["RANK.EQ or RANK.AVE function:\nref is all the values,\nnumber is the particular value to rank,\n0 = big to small (order)"]
["Screen shot of Rank & Percentile Excel tool:"]

{"sheet":"V(1)","index":30,"n_rows":74,"n_cols":5,"rows_kept":74,"cut":false,"hidden":false,"merged":[]}
["Which Mean More Fairly Represents Its Data Points?"]
[]
["Month","# of Hours/Week worked Job 1","# of Hours/Week worked Job 2"]
["2014-03-16",55,35]
[{"$f":"A4+7","v":"2014-03-23"},25,34]
[{"$f":"A5+7","v":"2014-03-30"},62,36]
["2014-04-06",15,37]
["2014-04-13",34,43]
["2014-04-20",19,32]
["2014-04-27",38,33]
["2014-05-04",29,35]
["2014-05-11",64,36]
["2014-05-18",31,37]
["2014-05-25",59,35]
["2014-06-01",12,39]
["2014-06-08",15,32]
["2014-06-15",42,31]
["2014-06-22",25,30]
["Mean",{"$f":"AVERAGE(B4:B18)","v":35},35]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
["X",{"$f":"B3","v":"# of Hours/Week worked Job 1"},null,"X",{"$f":"C3","v":"# of Hours/Week worked Job 2"}]
[{"$f":"B4","v":55},0.2,null,{"$f":"C4","v":35},0.7]
[25,0.2,null,34,0.7]
[62,0.2,null,36,0.7]
[15,0.2,null,37,0.7]
[34,0.2,null,43,0.7]
[19,0.2,null,32,0.7]
[38,0.2,null,33,0.7]
[29,0.2,null,35,0.7]
[64,0.2,null,36,0.7]
[31,0.2,null,37,0.7]
[59,0.2,null,35,0.7]
[12,0.2,null,39,0.7]
[15,0.2,null,32,0.7]
[42,0.2,null,31,0.7]
[25,0.2,null,30,0.7]
[]
["X","Ave Hours/Week Job 1",null,"X","Ave Hours/Week Job 2"]
[{"$f":"B19","v":35},0.35,null,{"$f":"C19","v":35},0.9]
[]
[]
[]
["Lower","Upper","Hour Bins","RF Job 1","RF Job 2"]
[10,{"$f":"A67+10","v":20},{"$f":"A67&\" up to \"&B67","v":"10 up to 20"},{"$f":"COUNTIFS(B$4:B$18,\">=\"&$A67,B$4:B$18,\"<\"&$B67)/$B$74","v":0.26666666666666666},0]
[{"$f":"B67","v":20},{"$f":"A68+10","v":30},"20 up to 30",0.2,0]
[{"$f":"B68","v":30},{"$f":"A69+10","v":40},"30 up to 40",0.2,0.93333333333333335]
[40,50,"40 up to 50",6.6666666666666666E-2,6.6666666666666666E-2]
[50,60,"50 up to 60",0.13333333333333333,0]
[60,70,"60 up to 70",0.13333333333333333,0]
[]
["Count",{"$f":"COUNT(A4:A18)","v":15}]

{"sheet":"V(2)","index":31,"n_rows":75,"n_cols":26,"rows_kept":75,"cut":false,"hidden":false,"merged":[]}
["Where are gas prices more volatile?",null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Gas Prices Burien","Gas Prices Seattle"]
[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,{"$f":"ROUND(CHOOSE(RANDBETWEEN(1,3),RANDBETWEEN(204,349),RANDBETWEEN(227,279),_xlfn.NORM.INV(RAND(),239,5))/100,2)","v":2.68},{"$f":"ROUND(CHOOSE(RANDBETWEEN(1,3),RANDBETWEEN(199,389),RANDBETWEEN(217,299),_xlfn.NORM.INV(RAND(),249,7))/100,2)","v":3.24}]
["Sample","Gas Prices Burien","Gas Prices Seattle"]
["Sample 1",3.01,3.86]
["Sample 2",2.35,2.93]
["Sample 3",2.4700000000000002,2.42]
["Sample 4",2.48,1.99]
["Sample 5",2.2799999999999998,2.5099999999999998]
["Sample 6",2.6,2.6]
["Sample 7",3.31,2.52]
["Sample 8",2.37,2.48]
["Sample 9",2.52,2.46]
["Sample 10",2.5,2.44]
["Sample 11",2.37,2.42]
["Sample 12",3.01,3.56]
["Sample 13",2.36,2.39]
["Sample 14",2.4500000000000002,2.79]
["Sample 15",2.38,2.58]
["Xbar",{"$f":"AVERAGE(B4:B18)","v":2.5640000000000005},2.6633333333333336]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
["X",{"$f":"B3","v":"Gas Prices Burien"},null,"X",{"$f":"C3","v":"Gas Prices Seattle"}]
[{"$f":"B4","v":3.01},0.2,null,{"$f":"C4","v":3.86},0.7]
[2.35,0.2,null,2.93,0.7]
[2.4700000000000002,0.2,null,2.42,0.7]
[2.48,0.2,null,1.99,0.7]
[2.2799999999999998,0.2,null,2.5099999999999998,0.7]
[2.6,0.2,null,2.6,0.7]
[3.31,0.2,null,2.52,0.7]
[2.37,0.2,null,2.48,0.7]
[2.52,0.2,null,2.46,0.7]
[2.5,0.2,null,2.44,0.7]
[2.37,0.2,null,2.42,0.7]
[3.01,0.2,null,3.56,0.7]
[2.36,0.2,null,2.39,0.7]
[2.4500000000000002,0.2,null,2.79,0.7]
[2.38,0.2,null,2.58,0.7]
[]
["X",{"$f":"\"Mean \"&B40","v":"Mean Gas Prices Burien"},null,"X",{"$f":"\"Mean \"&E40","v":"Mean Gas Prices Seattle"}]
[{"$f":"B19","v":2.5640000000000005},0.35,null,{"$f":"C19","v":2.6633333333333336},0.9]
[]
[]
[]
["Lower","Upper","Return Bins","RF Stock A","RF Stock B"]
[1.8,{"$f":"A63+0.2","v":2},{"$f":"TEXT(A63,\"$0.00\")&CHAR(10)&\"up to\"&CHAR(10)&TEXT(B63,\"$0.00\")","v":"$1.80\nup to\n$2.00"},{"$f":"COUNTIFS(B$4:B$18,\">=\"&$A63,B$4:B$18,\"<\"&$B63)/$B$75","v":0},6.6666666666666666E-2]
[{"$f":"B63","v":2},{"$f":"A64+0.2","v":2.2000000000000002},{"$f":"TEXT(A64,\"$0.00\")&CHAR(10)&\"up to\"&CHAR(10)&TEXT(B64,\"$0.00\")","v":"$2.00\nup to\n$2.20"},0,0]
[2.2000000000000002,2.4000000000000004,"$2.20\nup to\n$2.40",0.4,6.6666666666666666E-2]
[2.4000000000000004,2.6000000000000005,"$2.40\nup to\n$2.60",0.33333333333333331,0.53333333333333333]
[2.6000000000000005,2.8000000000000007,"$2.60\nup to\n$2.80",6.6666666666666666E-2,0.13333333333333333]
[2.8000000000000007,3.0000000000000009,"$2.80\nup to\n$3.00",0,6.6666666666666666E-2]
[3.0000000000000009,3.2000000000000011,"$3.00\nup to\n$3.20",0.13333333333333333,0]
[3.2000000000000011,3.4000000000000012,"$3.20\nup to\n$3.40",6.6666666666666666E-2,0]
[{"$f":"B70","v":3.4000000000000012},3.6000000000000014,"$3.40\nup to\n$3.60",{"$f":"COUNTIFS(B$4:B$18,\">=\"&$A71,B$4:B$18,\"<\"&$B71)/$B$75","v":0},{"$f":"COUNTIFS(C$4:C$18,\">=\"&$A71,C$4:C$18,\"<\"&$B71)/$B$75","v":6.6666666666666666E-2}]
[3.6000000000000014,3.8000000000000016,"$3.60\nup to\n$3.80",0,0]
[3.8000000000000016,4.0000000000000018,"$3.80\nup to\n$4.00",0,6.6666666666666666E-2]
[]
["Count",{"$f":"COUNTA(A4:A18)","v":15}]

{"sheet":"V(3)","index":32,"n_rows":85,"n_cols":5,"rows_kept":85,"cut":false,"hidden":false,"merged":[]}
["Based on Average Days To Fill Order, which supplier is more reliable?"]
[]
["Order #","Days to fill order Banderson's","Days to fill order Plywood & Floor"]
["Order 1",4,10]
["Order 2",6,11]
["Order 3",12,9]
["Order 4",11,9]
["Order 5",13,10]
["Order 6",16,8]
["Order 7",5,9]
["Order 8",8,10]
["Order 9",9,10]
["Order 10",6,9]
["Order 11",8,8]
["Order 12",6,9]
["Order 13",12,6]
["Order 14",6,8]
["Order 15",13,9]
["Mean",{"$f":"AVERAGE(B4:B18)","v":9},9]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
["X",{"$f":"B3","v":"Days to fill order Banderson's"},null,"X",{"$f":"C3","v":"Days to fill order Plywood & Floor"}]
[{"$f":"B4","v":4},0.2,null,{"$f":"C4","v":10},0.7]
[6,0.2,null,11,0.7]
[12,0.2,null,9,0.7]
[11,0.2,null,9,0.7]
[13,0.2,null,10,0.7]
[16,0.2,null,8,0.7]
[5,0.2,null,9,0.7]
[8,0.2,null,10,0.7]
[9,0.2,null,10,0.7]
[6,0.2,null,9,0.7]
[8,0.2,null,8,0.7]
[6,0.2,null,9,0.7]
[12,0.2,null,6,0.7]
[6,0.2,null,8,0.7]
[13,0.2,null,9,0.7]
[]
["X","Ave Days To Fill Order, Banderson's",null,"X","Ave Days To Fill Order, Plywood & Floor"]
[{"$f":"B19","v":9},0.35,null,{"$f":"C19","v":9},0.9]
[]
[]
["Days","RF Banderson's","RF Plywood & Floor"]
[4,{"$f":"COUNTIFS(B$4:B$18,$A70)/$B$85","v":6.6666666666666666E-2},0]
[5,6.6666666666666666E-2,0]
[6,0.26666666666666666,6.6666666666666666E-2]
[7,0,0]
[8,0.13333333333333333,0.2]
[9,6.6666666666666666E-2,0.4]
[10,0,0.26666666666666666]
[11,6.6666666666666666E-2,6.6666666666666666E-2]
[12,0.13333333333333333,0]
[13,0.13333333333333333,0]
[14,0,0]
[15,0,0]
[16,6.6666666666666666E-2,0]
["Total",{"$f":"SUM(B70:B82)","v":0.99999999999999989},{"$f":"SUM(C70:C82)","v":1}]
[]
["Count",{"$f":"COUNTA(A4:A18)","v":15}]

{"sheet":"V(4)","index":33,"n_rows":28,"n_cols":7,"rows_kept":28,"cut":false,"hidden":false,"merged":[[7,0,7,6],[6,0,6,6],[0,0,0,6],[1,0,1,6],[2,0,2,6],[3,0,3,6],[4,0,4,6],[5,0,5,6]]}
["Variability"]
["Dispersion"]
["Spread In Data"]
["How Spread Out Is Data?"]
["Are the Data Points Clustered Around the Mean?"]
["Does the Mean Fairly Represent the Data Points?"]
["Visually Portray Variability"]
["Create X-Y Scatter Diagram"]
[]
[null,null,"X","Mean Days for S 1",null,"X","Mean Days for S 2"]
["Mean",null,{"$f":"AVERAGE(C14:C28)","v":9},null,null,{"$f":"AVERAGE(F14:F28)","v":9}]
[]
["Order #",null,"X","Days to fill order Banderson's",null,"X","Days to fill order Plywood & Floor"]
["Order 1",null,4,null,null,10]
["Order 2",null,6,null,null,11]
["Order 3",null,12,null,null,9]
["Order 4",null,11,null,null,9]
["Order 5",null,13,null,null,10]
["Order 6",null,16,null,null,8]
["Order 7",null,5,null,null,9]
["Order 8",null,8,null,null,10]
["Order 9",null,9,null,null,10]
["Order 10",null,6,null,null,9]
["Order 11",null,8,null,null,8]
["Order 12",null,6,null,null,9]
["Order 13",null,12,null,null,6]
["Order 14",null,6,null,null,8]
["Order 15",null,13,null,null,9,1]

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["Variability"]
["Dispersion"]
["Spread In Data"]
["How Spread Out Is Data?"]
["Are the Data Points Clustered Around the Mean?"]
["Does the Mean Fairly Represent the Data Points?"]
["Visually Portray Variability"]
["Create X-Y Scatter Diagram"]
[]
[null,null,"X","Mean Days for S 1",null,"X","Mean Days for S 2"]
["Mean",null,{"$f":"AVERAGE(C14:C28)","v":9},0.75,null,{"$f":"AVERAGE(F14:F28)","v":9},1.25]
[]
["Order #",null,"X","Days to fill order Banderson's",null,"X","Days to fill order Plywood & Floor"]
["Order 1",null,4,0.5,null,10,1]
["Order 2",null,6,0.5,null,11,1]
["Order 3",null,12,0.5,null,9,1]
["Order 4",null,11,0.5,null,9,1]
["Order 5",null,13,0.5,null,10,1]
["Order 6",null,16,0.5,null,8,1]
["Order 7",null,5,0.5,null,9,1]
["Order 8",null,8,0.5,null,10,1]
["Order 9",null,9,0.5,null,10,1]
["Order 10",null,6,0.5,null,9,1]
["Order 11",null,8,0.5,null,8,1]
["Order 12",null,6,0.5,null,9,1]
["Order 13",null,12,0.5,null,6,1]
["Order 14",null,6,0.5,null,8,1]
["Order 15",null,13,0.5,null,9,1]

{"sheet":"V(5)","index":35,"n_rows":31,"n_cols":8,"rows_kept":31,"cut":false,"hidden":false,"merged":[]}
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
["Order #","Days to fill order Banderson's","Days to fill order Plywood & Floor",null,"Range"]
["Order 1",4,10,null,"Max - Min"]
["Order 2",6,11,null,"Simple to calculate. It is very sensitive to the smallest and largest data values (extreme values)."]
["Order 3",12,9]
["Order 4",11,9,null,null,{"$f":"B15","v":"Days to fill order Banderson's"},{"$f":"C15","v":"Days to fill order Plywood & Floor"}]
["Order 5",13,10,null,"Max"]
["Order 6",16,8,null,"Min"]
["Order 7",5,9,null,"Range"]
["Order 8",8,10]
["Order 9",9,10,null,"Interquartile Range"]
["Order 10",6,9,null,"Quartile 3 - Quartile 1"]
["Order 11",8,8,null,"The range for the middle 50% of the data. It overcomes the sensitivity to extreme data values."]
["Order 12",6,9]
["Order 13",12,6,null,{"$f":"\"Quartile \"&H28","v":"Quartile 1"},null,null,1]
["Order 14",6,8,null,{"$f":"\"Quartile \"&H29","v":"Quartile 3"},null,null,3]
["Order 15",13,9,null,"Interquartile Range"]
["Mean",{"$f":"AVERAGE(B16:B30)","v":9},9]

{"sheet":"V(5an)","index":36,"n_rows":31,"n_cols":8,"rows_kept":31,"cut":false,"hidden":false,"merged":[]}
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
["Order #","Days to fill order Banderson's","Days to fill order Plywood & Floor",null,"Range"]
["Order 1",4,10,null,"Max - Min"]
["Order 2",6,11,null,"Simple to calculate. It is very sensitive to the smallest and largest data values (extreme values)."]
["Order 3",12,9]
["Order 4",11,9,null,null,{"$f":"B15","v":"Days to fill order Banderson's"},{"$f":"C15","v":"Days to fill order Plywood & Floor"}]
["Order 5",13,10,null,"Max",{"$f":"MAX(B16:B30)","v":16},{"$f":"MAX(C16:C30)","v":11}]
["Order 6",16,8,null,"Min",{"$f":"MIN(B16:B30)","v":4},{"$f":"MIN(C16:C30)","v":6}]
["Order 7",5,9,null,"Range",{"$f":"F20-F21","v":12},5]
["Order 8",8,10]
["Order 9",9,10,null,"Interquartile Range"]
["Order 10",6,9,null,"Quartile 3 - Quartile 1"]
["Order 11",8,8,null,"The range for the middle 50% of the data. It overcomes the sensitivity to extreme data values."]
["Order 12",6,9]
["Order 13",12,6,null,{"$f":"\"Quartile \"&H28","v":"Quartile 1"},{"$f":"_xlfn.QUARTILE.EXC(B$16:B$30,$H28)","v":6},{"$f":"_xlfn.QUARTILE.EXC(C$16:C$30,$H28)","v":8},1]
["Order 14",6,8,null,{"$f":"\"Quartile \"&H29","v":"Quartile 3"},{"$f":"_xlfn.QUARTILE.EXC(B$16:B$30,$H29)","v":12},{"$f":"_xlfn.QUARTILE.EXC(C$16:C$30,$H29)","v":10},3]
["Order 15",13,9,null,"Interquartile Range",{"$f":"F29-F28","v":6},2]
["Mean",{"$f":"AVERAGE(B16:B30)","v":9},9]

{"sheet":"V(6)","index":37,"n_rows":32,"n_cols":11,"rows_kept":32,"cut":false,"hidden":false,"merged":[]}
["Variability"]
["Dispersion"]
["Spread In Data"]
["How Spread Out Is Data?"]
["Are the Data Points Clustered Around the Mean?"]
["Does the Mean Fairly Represent the Data Points?"]
["Range"]
["Max - Min"]
["Simple to calculate. Sensitive to extreme values"]
["Interquartile Range"]
["Quartile 3 - Quartile 1",null,null,null,null,null,null,null,null,null,"These two not required for this class:"]
["The range for the middle 50% of the data. It overcomes the sensitivity to extreme values"]
["Deviation",null,null,null,null,null,null,null,null,null,"Alternative for variability measure called Mean Absolute Error"]
["How far one datum is from the mean. \"How far above or below the mean is the particular value?\""]
["\"Deviation about the mean\""]
["For any data set, the sum of the deviations is always zero!!!! This is why mathematically, we either square (Variance or Standard Deviation) or take the absolute value (Mean Absolute Value)"]
["Variance"]
["A Numerical Measure that says how much variability there is in the data points"]
["Variance uses all the data points, not just some like Range and Interquartile Range"]
["Variance has squared units, which makes interpreting it difficult. Standard Deviation undoes the squared units and is thus easier to interpret and is more commonly used",null,null,null,null,null,null,null,null,null,"ALTERNATIVE TO CALCULATE Sample SD"]
["Use STDEV. Function"]
["Standard Deviation = SD"]
["Standard Deviation uses all the data points, not just some like Range and Interquartile Range"]
["Standard Deviation does not have squared units (like Variance) and is thus easier to interpret; the standard deviation has the same units as the data!!"]
["Population SD = \"little sigma\" = s ; Sample SD = s",null,null,null,"Xbar"," =","Sample Mean","\"Xbar\""]
["The sample standard deviation is a point estimator of the population standard deviation",null,null,null,"µ"," =","Population Mean","\"Mew\""]
["Interpretation of Standard Deviation:",null,null,null,"s"," =","Sample Standard Deviation","\"s\""]
["A Numerical Measure that says how much variability there is in the data points",null,null,null,"s"," =","Sample Standard Deviation","\"sigma\""]
["Standard Deviation Is Like An Average Of The Deviations",null,null,null,"xi"," =","Particular Value","\"x sub i\""]
["Standard Deviation tells us how fairly the mean represents its data points",null,null,null,"∑"," =","Greek Letter used for \"adding\"","\"Sigma\""]
["Standard Deviation tells us how clustered the data points are around the mean",null,null,null,"n"," =","Count of sample items","\"n\""]
["For financial assets standard deviation is a measure of risk or fluctuation in asset value",null,null,null,"N"," =","Count of population items","\"n\""]

{"sheet":"V(7)","index":38,"n_rows":166,"n_cols":8,"rows_kept":166,"cut":false,"hidden":false,"merged":[[6,0,6,6],[8,0,8,6],[0,0,0,6],[1,0,1,6],[2,0,2,6],[3,0,3,6],[4,0,4,6],[5,0,5,6]]}
["Deviation"]
["How far one datum is from the mean"]
["Sample Standard Deviation"]
["A Numerical Measure that says how much variability there is in the data points"]
["Standard Deviation Is Like An Average Of The Deviations"]
["Standard Deviation tells us how fairly the mean represents its data points"]
["Standard Deviation tells us how clustered the data points are around the mean"]
[]
["Based on Average Days to Fill Order, which Supplier do you prefer?\nIt is hard to know, without looking at the volatility of past delivery times."]
["Count"]
["Count -1",null,null,null,null,"Sample data divided by just the count underestimates"]
["Mean = Xbar",null,null,null,null,"What minus one does: 10/5 = 2, but 10/(5-1) = 2.5"]
["Order #","Days to fill order Banderson's, xi","Deviation\nXi - Xbar","Deviation ^2\n(Xi - Xbar)^2","Days to fill order Plywood & Floor","Deviation\nXi - Xbar","Deviation ^2\n(Xi - Xbar)^2"]
["Order 1",4,null,null,10]
["Order 2",6,null,null,11]
["Order 3",12,null,null,9]
["Order 4",11,null,null,9]
["Order 5",13,null,null,10]
["Order 6",16,null,null,8]
["Order 7",5,null,null,9]
["Order 8",8,null,null,10]
["Order 9",9,null,null,10]
["Order 10",6,null,null,9]
["Order 11",8,null,null,8]
["Order 12",6,null,null,9]
["Order 13",12,null,null,6]
["Order 14",6,null,null,8]
["Order 15",13,null,null,9]
[null,"Total"]
["Sum Deviations ALWAYS = 0!"]
["Why we have to square!!!"]
[null,"Sample Variance = s2",null,null,null,null,null,"Units are squared"]
[null,"Sample Variance = s2",null,null,null,null,null,"VAR.S (Variance for Sample)"]
[null,"Sample Standard Deviation = s",null,null,null,null,null,"Square root to get back to same unit"]
[null,null,null,null,null,null,null,"STDEV.S (Standard Deviation for Sample)"]
[null,null,null,null,null,null,null,"The sample SD, s, is a point estimator  of the population SD, sigma σ"]
[null,null,null,null,null,null,null,"Which mean more fairly represents its data points?"]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
["ALTERNATIVE TO CALCULATE Sample SD"]
[]
[]
[]
[]
[]
["Count",{"$f":"COUNT(B72:B86)","v":15},null,null,{"$f":"COUNT(E72:E86)","v":15}]
["Count -1",{"$f":"B68-1","v":14},null,null,{"$f":"E68-1","v":14}]
["Mean = Xbar",{"$f":"AVERAGE(B72:B86)","v":9},null,null,{"$f":"AVERAGE(E72:E86)","v":9}]
["Order #","Days to fill order Banderson's, xi","xi^2",null,"Days to fill order Plywood & Floor","xi^2"]
["Order 1",4,{"$f":"B72^2","v":16},null,10,{"$f":"E72^2","v":100}]
["Order 2",6,36,null,11,121]
["Order 3",12,144,null,9,81]
["Order 4",11,121,null,9,81]
["Order 5",13,169,null,10,100]
["Order 6",16,256,null,8,64]
["Order 7",5,25,null,9,81]
["Order 8",8,64,null,10,100]
["Order 9",9,81,null,10,100]
["Order 10",6,36,null,9,81]
["Order 11",8,64,null,8,64]
["Order 12",6,36,null,9,81]
["Order 13",12,144,null,6,36]
["Order 14",6,36,null,8,64]
["Order 15",13,169,null,9,81]
[]
[null,"Total",{"$f":"SUM(C72:C86)","v":1397},null,"Total",{"$f":"SUM(F72:F86)","v":1235}]
[null,"s",{"$f":"SQRT((C88-B68*B70^2)/B69)","v":3.6055512754639891},null,"s",{"$f":"SQRT((F88-E68*E70^2)/E69)","v":1.1952286093343936}]
[]
[]
["Alternative for variability measure called Mean Absolute Error"]
[]
[]
[]
[]
["Count",{"$f":"COUNT(B100:B114)","v":15},null,null,{"$f":"COUNT(E100:E114)","v":15}]
["Mean = Xbar",{"$f":"AVERAGE(B100:B114)","v":9},null,null,{"$f":"AVERAGE(E100:E114)","v":9}]
["Order #","Days to fill order Banderson's, xi","ABS(Xi - Xbar)",null,"Days to fill order Plywood & Floor","ABS(Xi - Xbar)"]
["Order 1",4,{"$f":"ABS(B100-B$98)","v":5},null,10,{"$f":"ABS(E100-E$98)","v":1}]
["Order 2",6,3,null,11,2]
["Order 3",12,3,null,9,0]
["Order 4",11,2,null,9,0]
["Order 5",13,4,null,10,1]
["Order 6",16,7,null,8,1]
["Order 7",5,4,null,9,0]
["Order 8",8,1,null,10,1]
["Order 9",9,0,null,10,1]
["Order 10",6,3,null,9,0]
["Order 11",8,1,null,8,1]
["Order 12",6,3,null,9,0]
["Order 13",12,3,null,6,3]
["Order 14",6,3,null,8,1]
["Order 15",13,4,null,9,0]
[]
[null,"Total",{"$f":"SUM(C100:C114)","v":46},null,"Total",{"$f":"SUM(F100:F114)","v":12}]
[null,"s",{"$f":"C116/B97","v":3.0666666666666669},null,"s",{"$f":"F116/E97","v":0.8}]
[null,null,{"$f":"AVEDEV(B100:B114\n)","v":3.0666666666666669},null,null,{"$f":"AVEDEV(E100:E114\n)","v":0.8},"AVEDEV (Mean Absolute Error or Average Absolute Deviations)"]
[]
[]
[]
[]
["Which Mean More Fairly Represents Its Data Points?"]
[]
["Month","# of Hours/Week worked Job 1","# of Hours/Week worked Job 2"]
["2014-03-16",55,35]
[{"$f":"A126+7","v":"2014-03-23"},25,34]
[{"$f":"A127+7","v":"2014-03-30"},62,36]
["2014-04-06",15,37]
["2014-04-13",34,43]
["2014-04-20",19,32]
["2014-04-27",38,33]
["2014-05-04",29,35]
["2014-05-11",64,36]
["2014-05-18",31,37]
["2014-05-25",59,35]
["2014-06-01",12,39]
["2014-06-08",15,32]
["2014-06-15",42,31]
["2014-06-22",25,30]
["Mean",{"$f":"AVERAGE(B126:B140)","v":35},35]
[]
["Sample SD"]
[]
[]
["Where are gas prices more volatile?"]
[]
["Sample","Gas Prices Burien","Gas Prices Seattle"]
["Sample 1",3.01,3.86]
["Sample 2",2.35,2.93]
["Sample 3",2.4700000000000002,2.42]
["Sample 4",2.48,1.99]
["Sample 5",2.2799999999999998,2.5099999999999998]
["Sample 6",2.6,2.6]
["Sample 7",3.31,2.52]
["Sample 8",2.37,2.48]
["Sample 9",2.52,2.46]
["Sample 10",2.5,2.44]
["Sample 11",2.37,2.42]
["Sample 12",3.01,3.56]
["Sample 13",2.36,2.39]
["Sample 14",2.4500000000000002,2.79]
["Sample 15",2.38,2.58]
["Xbar",{"$f":"AVERAGE(B149:B163)","v":2.5640000000000005},2.6633333333333336]
[]
["Sample SD"]

{"sheet":"V(7an)","index":39,"n_rows":166,"n_cols":8,"rows_kept":166,"cut":false,"hidden":false,"merged":[[6,0,6,6],[8,0,8,6],[0,0,0,6],[1,0,1,6],[2,0,2,6],[3,0,3,6],[4,0,4,6],[5,0,5,6]]}
["Deviation"]
["How far one datum is from the mean"]
["Sample Standard Deviation"]
["A Numerical Measure that says how much variability there is in the data points"]
["Standard Deviation Is Like An Average Of The Deviations"]
["Standard Deviation tells us how fairly the mean represents its data points"]
["Standard Deviation tells us how clustered the data points are around the mean"]
[]
["Based on Average Days to Fill Order, which Supplier do you prefer?\nIt is hard to know, without looking at the volatility of past delivery times."]
["Count",{"$f":"COUNT(B14:B28)","v":15},null,null,{"$f":"COUNT(E14:E28)","v":15}]
["Count -1",{"$f":"B10-1","v":14},null,null,{"$f":"E10-1","v":14},"Sample data divided by just the count underestimates"]
["Mean = Xbar",{"$f":"AVERAGE(B14:B28)","v":9},null,null,{"$f":"AVERAGE(E14:E28)","v":9},"What minus one does: 10/5 = 2, but 10/(5-1) = 2.5"]
["Order #","Days to fill order Banderson's, xi","Deviation\nXi - Xbar","Deviation ^2\n(Xi - Xbar)^2","Days to fill order Plywood & Floor","Deviation\nXi - Xbar","Deviation ^2\n(Xi - Xbar)^2"]
["Order 1",4,{"$f":"B14-B$12","v":-5},{"$f":"C14^2","v":25},10,{"$f":"E14-E$12","v":1},{"$f":"F14^2","v":1}]
["Order 2",6,-3,9,11,2,4]
["Order 3",12,3,9,9,0,0]
["Order 4",11,2,4,9,0,0]
["Order 5",13,4,16,10,1,1]
["Order 6",16,7,49,8,-1,1]
["Order 7",5,-4,16,9,0,0]
["Order 8",8,-1,1,10,1,1]
["Order 9",9,0,0,10,1,1]
["Order 10",6,-3,9,9,0,0]
["Order 11",8,-1,1,8,-1,1]
["Order 12",6,-3,9,9,0,0]
["Order 13",12,3,9,6,-3,9]
["Order 14",6,-3,9,8,-1,1]
["Order 15",13,4,16,9,0,0]
[null,"Total",{"$f":"SUM(C14:C28)","v":0},{"$f":"SUM(D14:D28)","v":182},null,{"$f":"SUM(F14:F28)","v":0},{"$f":"SUM(G14:G28)","v":20}]
["Sum Deviations ALWAYS = 0!"]
["Why we have to square!!!"]
[null,"Sample Variance = s2",null,{"$f":"D29/B11","v":13},null,null,{"$f":"G29/E11","v":1.4285714285714286},"Units are squared"]
[null,"Sample Variance = s2",null,{"$f":"_xlfn.VAR.S(B14:B28)","v":13},null,null,{"$f":"_xlfn.VAR.S(E14:E28)","v":1.4285714285714286},"VAR.S (Variance for Sample)"]
[null,"Sample Standard Deviation = s",null,{"$f":"SQRT(D32)","v":3.6055512754639891},null,null,{"$f":"SQRT(G32)","v":1.1952286093343936},"Square root to get back to same unit"]
[null,null,null,{"$f":"_xlfn.STDEV.S(B14:B28)","v":3.6055512754639891},null,null,{"$f":"_xlfn.STDEV.S(E14:E28)","v":1.1952286093343936},"STDEV.S (Standard Deviation for Sample)"]
[null,null,null,null,null,null,null,"The sample SD, s, is a point estimator  of the population SD, sigma σ"]
[null,null,null,null,null,null,null,"Which mean more fairly represents its data points?"]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
["ALTERNATIVE TO CALCULATE Sample SD"]
[]
[]
[]
[]
[]
["Count",{"$f":"COUNT(B72:B86)","v":15},null,null,{"$f":"COUNT(E72:E86)","v":15}]
["Count -1",{"$f":"B68-1","v":14},null,null,{"$f":"E68-1","v":14}]
["Mean = Xbar",{"$f":"AVERAGE(B72:B86)","v":9},null,null,{"$f":"AVERAGE(E72:E86)","v":9}]
["Order #","Days to fill order Banderson's, xi","xi^2",null,"Days to fill order Plywood & Floor","xi^2"]
["Order 1",4,{"$f":"B72^2","v":16},null,10,{"$f":"E72^2","v":100}]
["Order 2",6,36,null,11,121]
["Order 3",12,144,null,9,81]
["Order 4",11,121,null,9,81]
["Order 5",13,169,null,10,100]
["Order 6",16,256,null,8,64]
["Order 7",5,25,null,9,81]
["Order 8",8,64,null,10,100]
["Order 9",9,81,null,10,100]
["Order 10",6,36,null,9,81]
["Order 11",8,64,null,8,64]
["Order 12",6,36,null,9,81]
["Order 13",12,144,null,6,36]
["Order 14",6,36,null,8,64]
["Order 15",13,169,null,9,81]
[]
[null,"Total",{"$f":"SUM(C72:C86)","v":1397},null,"Total",{"$f":"SUM(F72:F86)","v":1235}]
[null,"s",{"$f":"SQRT((C88-B68*B70^2)/B69)","v":3.6055512754639891},null,"s",{"$f":"SQRT((F88-E68*E70^2)/E69)","v":1.1952286093343936}]
[]
[]
["Alternative for variability measure called Mean Absolute Error"]
[]
[]
[]
[]
["Count",{"$f":"COUNT(B100:B114)","v":15},null,null,{"$f":"COUNT(E100:E114)","v":15}]
["Mean = Xbar",{"$f":"AVERAGE(B100:B114)","v":9},null,null,{"$f":"AVERAGE(E100:E114)","v":9}]
["Order #","Days to fill order Banderson's, xi","ABS(Xi - Xbar)",null,"Days to fill order Plywood & Floor","ABS(Xi - Xbar)"]
["Order 1",4,{"$f":"ABS(B100-B$98)","v":5},null,10,{"$f":"ABS(E100-E$98)","v":1}]
["Order 2",6,3,null,11,2]
["Order 3",12,3,null,9,0]
["Order 4",11,2,null,9,0]
["Order 5",13,4,null,10,1]
["Order 6",16,7,null,8,1]
["Order 7",5,4,null,9,0]
["Order 8",8,1,null,10,1]
["Order 9",9,0,null,10,1]
["Order 10",6,3,null,9,0]
["Order 11",8,1,null,8,1]
["Order 12",6,3,null,9,0]
["Order 13",12,3,null,6,3]
["Order 14",6,3,null,8,1]
["Order 15",13,4,null,9,0]
[]
[null,"Total",{"$f":"SUM(C100:C114)","v":46},null,"Total",{"$f":"SUM(F100:F114)","v":12}]
[null,"s",{"$f":"C116/B97","v":3.0666666666666669},null,"s",{"$f":"F116/E97","v":0.8}]
[null,null,{"$f":"AVEDEV(B100:B114\n)","v":3.0666666666666669},null,null,{"$f":"AVEDEV(E100:E114\n)","v":0.8},"AVEDEV (Mean Absolute Error or Average Absolute Deviations)"]
[]
[]
[]
[]
["Which Mean More Fairly Represents Its Data Points?"]
[]
["Month","# of Hours/Week worked Job 1","# of Hours/Week worked Job 2"]
["2014-03-16",55,35]
[{"$f":"A126+7","v":"2014-03-23"},25,34]
[{"$f":"A127+7","v":"2014-03-30"},62,36]
["2014-04-06",15,37]
["2014-04-13",34,43]
["2014-04-20",19,32]
["2014-04-27",38,33]
["2014-05-04",29,35]
["2014-05-11",64,36]
["2014-05-18",31,37]
["2014-05-25",59,35]
["2014-06-01",12,39]
["2014-06-08",15,32]
["2014-06-15",42,31]
["2014-06-22",25,30]
["Mean",{"$f":"AVERAGE(B126:B140)","v":35},35]
[]
["Sample SD"]
[]
[]
["Where are gas prices more volatile?"]
[]
["Sample","Gas Prices Burien","Gas Prices Seattle"]
["Sample 1",3.01,3.86]
["Sample 2",2.35,2.93]
["Sample 3",2.4700000000000002,2.42]
["Sample 4",2.48,1.99]
["Sample 5",2.2799999999999998,2.5099999999999998]
["Sample 6",2.6,2.6]
["Sample 7",3.31,2.52]
["Sample 8",2.37,2.48]
["Sample 9",2.52,2.46]
["Sample 10",2.5,2.44]
["Sample 11",2.37,2.42]
["Sample 12",3.01,3.56]
["Sample 13",2.36,2.39]
["Sample 14",2.4500000000000002,2.79]
["Sample 15",2.38,2.58]
["Xbar",{"$f":"AVERAGE(B149:B163)","v":2.5640000000000005},2.6633333333333336]
[]
["Sample SD"]

{"sheet":"V(8)","index":40,"n_rows":114,"n_cols":22,"rows_kept":114,"cut":false,"hidden":false,"merged":[]}
[null,"Set 1",null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Create 1","Create 2","Sample1","Sample2"]
["Mean",{"$f":"AVERAGE(A11:A33)","v":15.586956521739131},null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,{"$f":"ROUND(CHOOSE(RANDBETWEEN(1,3),RANDBETWEEN(1100,2400),RANDBETWEEN(1300,1650),_xlfn.NORM.INV(RAND(),1325,100))/100,2)","v":16.47},{"$f":"ROUND(CHOOSE(RANDBETWEEN(1,3),RANDBETWEEN(1000,3200),RANDBETWEEN(1200,1950),_xlfn.NORM.INV(RAND(),1325,250))/100,2)","v":15.31},{"$f":"INDEX(C$11:C$114,RANDBETWEEN(1,COUNTA(C$11:C$114)))","v":16.309999999999999},{"$f":"INDEX(B$115:B$140,RANDBETWEEN(1,COUNTA(B$115:B$140)))","v":{"$error":"#NUM!"}}]
["Median",{"$f":"MEDIAN(A11:A33)","v":14.08}]
["Mode",{"$f":"_xlfn.MODE.SNGL(A11:A33)","v":22.12}]
["Sample Standard Deviation = s",{"$f":"_xlfn.STDEV.S(A11:A33)","v":3.5308747754404806},"STDEV.S",{"$f":"\"In Excel you can use the function:\"&CHAR(10)&C5&CHAR(10)&\"for calculating \"&A5","v":"In Excel you can use the function:\nSTDEV.S\nfor calculating Sample Standard Deviation = s"}]
["Population Standard Deviation = σ",{"$f":"_xlfn.STDEV.P(C$11:C$114)","v":3.6524411013075389},"STDEV.P",{"$f":"\"In Excel you can use the function:\"&CHAR(10)&C6&CHAR(10)&\"for calculating \"&A6","v":"In Excel you can use the function:\nSTDEV.P\nfor calculating Population Standard Deviation = σ"}]
[]
["Wage Data"]
[]
["Sample1",null,"Population 1"]
[22.35,null,15.35]
[14.01,null,32.159999999999997]
[15.2,null,12.55]
[18.3,null,13.74]
[13.63,null,22.35]
[14.08,null,15.4]
[15.44,null,14.3]
[10.75,null,14.79]
[14.67,null,20.94]
[11.29,null,11.91]
[15.02,null,12.4]
[17.97,null,12.05]
[22.12,null,18.11]
[22.12,null,14.83]
[20.68,null,13.93]
[13.21,null,11.66]
[18.739999999999998,null,12.69]
[13.66,null,14.01]
[12.94,null,12.52]
[12.94,null,14.35]
[13.34,null,15.2]
[13.68,null,12.68]
[12.36,null,12.32]
[null,null,15.65]
[null,null,18.02]
[null,null,14.55]
[null,null,18.3]
[null,null,13.63]
[null,null,17.47]
[null,null,15.62]
[null,null,13.71]
[null,null,14.87]
[null,null,14.08]
[null,null,13.03]
[null,null,20.51]
[null,null,12.73]
[null,null,15.66]
[null,null,13.58]
[null,null,15.89]
[null,null,15.44]
[null,null,14.65]
[null,null,14.37]
[null,null,10.75]
[null,null,11.99]
[null,null,18.63]
[null,null,14.67]
[null,null,11.29]
[null,null,15.66]
[null,null,14.32]
[null,null,26.81]
[null,null,14.03]
[null,null,15.02]
[null,null,14.93]
[null,null,16.16]
[null,null,21.79]
[null,null,15.49]
[null,null,19.850000000000001]
[null,null,12.75]
[null,null,17.97]
[null,null,10.97]
[null,null,22.12]
[null,null,20.68]
[null,null,20.68]
[null,null,14.3]
[null,null,16.13]
[null,null,12.41]
[null,null,22.53]
[null,null,21.66]
[null,null,19.079999999999998]
[null,null,14.88]
[null,null,22.52]
[null,null,20.97]
[null,null,15.42]
[null,null,11.46]
[null,null,13.03]
[null,null,14.53]
[null,null,17.21]
[null,null,12.68]
[null,null,12.02]
[null,null,13.21]
[null,null,18.739999999999998]
[null,null,15.89]
[null,null,13.42]
[null,null,13.66]
[null,null,13.11]
[null,null,15.93]
[null,null,15.01]
[null,null,12.94]
[null,null,16.309999999999999]
[null,null,12.87]
[null,null,13.71]
[null,null,20.43]
[null,null,13.44]
[null,null,15.94]
[null,null,14.32]
[null,null,15.22]
[null,null,22.59]
[null,null,12.32]
[null,null,13.34]
[null,null,14.52]
[null,null,23.7]
[null,null,13.21]
[null,null,13.68]
[null,null,12.36]

{"sheet":"V(9)","index":41,"n_rows":45,"n_cols":5,"rows_kept":45,"cut":false,"hidden":false,"merged":[[24,0,24,4],[25,0,25,4]]}
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
["Compare Relative Variability (Dispersion) in Two Distributions"]
[]
["Mean bonus paid per employee = Xbar",2000,null,"Mean years worked = Xbar",15]
["Standard Deviation = s",755,null,"Standard Deviation = s",3]
["Coefficient of Variance",null,null,"Coefficient of Variance"]
[]
["There is more variability relative to the mean in the distribution of bonus paid\ncompared with the distribution of years worked"]
["*Notice here that the units cancel out"]
[]
[]
["Mean Wage Data City 1",null,null,"Mean Wage Data City 2"]
["Standard Deviation, Wage Data City 1",null,null,"Standard Deviation, Wage Data City 2"]
["Coefficient of Variance",null,null,"Coefficient of Variance"]
[]
["Wage sample in City 1",null,null,"Wage sample in City 2"]
[12.54,null,null,25.5]
[15.68,null,null,23.68]
[19.579999999999998,null,null,78.25]
[25.71,null,null,25.12]
[45.68,null,null,12.5]
[15.69,null,null,19.850000000000001]
[14.78,null,null,45.54]
[35.5,null,null,35.979999999999997]
[19.57,null,null,15.24]
[18.71,null,null,110]
[35.450000000000003,null,null,28.58]
[12.75,null,null,35.21]

{"sheet":"V(9an)","index":42,"n_rows":45,"n_cols":5,"rows_kept":45,"cut":false,"hidden":false,"merged":[[24,0,24,4],[25,0,25,4]]}
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
["Compare Relative Variability (Dispersion) in Two Distributions"]
[]
["Mean bonus paid per employee = Xbar",2000,null,"Mean years worked = Xbar",15]
["Standard Deviation = s",755,null,"Standard Deviation = s",3]
["Coefficient of Variance",{"$f":"B22/B21","v":0.3775},null,"Coefficient of Variance",{"$f":"E22/E21","v":0.2}]
[]
["There is more variability relative to the mean in the distribution of bonus paid\ncompared with the distribution of years worked"]
["*Notice here that the units cancel out"]
[]
[]
["Mean Wage Data City 1",{"$f":"AVERAGE(A34:A45)","v":22.636666666666667},null,"Mean Wage Data City 2",{"$f":"AVERAGE(D34:D45)","v":37.954166666666666}]
["Standard Deviation, Wage Data City 1",{"$f":"_xlfn.STDEV.S(A34:A45)","v":10.712445042467147},null,"Standard Deviation, Wage Data City 2",{"$f":"_xlfn.STDEV.S(D34:D45)","v":28.582236523704193}]
["Coefficient of Variance",{"$f":"B30/B29","v":0.47323420891476131},null,"Coefficient of Variance",{"$f":"E30/E29","v":0.75307243008991176}]
[]
["Wage sample in City 1",null,null,"Wage sample in City 2"]
[12.54,null,null,25.5]
[15.68,null,null,23.68]
[19.579999999999998,null,null,78.25]
[25.71,null,null,25.12]
[45.68,null,null,12.5]
[15.69,null,null,19.850000000000001]
[14.78,null,null,45.54]
[35.5,null,null,35.979999999999997]
[19.57,null,null,15.24]
[18.71,null,null,110]
[35.450000000000003,null,null,28.58]
[12.75,null,null,35.21]

{"sheet":"V(10)","index":43,"n_rows":37,"n_cols":14,"rows_kept":37,"cut":false,"hidden":false,"merged":[]}
["Number of Days to Fill Order for Custom Birch Plywood for boomerang manufacturer"]
[]
[null,"Banderson's","Plywood & Floor"]
[null,4,10,null,null,null,null,"Days",{"$f":"\"Count for \"&B3","v":"Count for Banderson's"},{"$f":"\"Count for \"&C3","v":"Count for Plywood & Floor"}]
[null,6,11,null,null,null,null,4,{"$f":"COUNTIFS(B$4:B$18,$H5)","v":1},{"$f":"-COUNTIFS(C$4:C$18,$H5)","v":0}]
[null,12,9,null,null,null,null,5,1,0]
[null,11,9,null,null,null,null,6,4,-1]
[null,13,10,null,null,null,null,7,0,0]
[null,16,8,null,null,null,null,8,2,-3]
[null,5,9,null,null,null,null,9,1,-6]
[null,8,10,null,null,null,null,10,0,-4]
[null,9,10,null,null,null,null,11,1,-1]
[null,6,9,null,null,null,null,12,2,0]
[null,8,8,null,null,null,null,13,2,0]
[null,6,9,null,null,null,null,14,0,0]
[null,12,6,null,null,null,null,15,0,0]
[null,6,8,null,null,null,null,16,1,0]
[null,13,9]
[null,null,null,null,"Units",null,null,"Total",{"$f":"COUNT(B4:B18)","v":15}]
["Xbar",{"$f":"AVERAGE(B4:B18)","v":9},{"$f":"AVERAGE(C4:C18)","v":9},null,"Days"]
["Max",{"$f":"MAX(B4:B18)","v":16},11,null,"Days"]
["Min",{"$f":"MIN(B4:B18)","v":4},6,null,"Days"]
["Range",{"$f":"B21-B22","v":12},5,null,"Days"]
["Sample SD = s",{"$f":"_xlfn.STDEV.S(B4:B18)","v":3.6055512754639891},1.1952286093343936,null,"Days"]
[]
["Although they both deliver in an average of 9 days, Banderson's has a standard deviation of"]
["about 3.5 days and Plywood and Floor has a standard deviation of about 1.2 days."]
["For planning business activities, a smaller standard Deviation (average deviation) is more"]
["helpful and reliable."]
["A smaller standard deviation indicates that there is less variation in the number of"]
["days it will take to deliver the custom birch plywood."]
[]
[]
[]
[{"$f":"B3","v":"Banderson's"},{"$f":"REPT(\"*\",COUNTIFS($B$4:$B$18,B37))","v":"*"},"*","****","","**","*","","*","**","**","","","*"]
[{"$f":"C3","v":"Plywood & Floor"},{"$f":"REPT(\"*\",COUNTIFS($C$4:$C$18,B37))","v":""},{"$f":"REPT(\"*\",COUNTIFS($C$4:$C$18,C37))","v":""},"*","","***","******","****","*","","","","",""]
["Days To Fill Order",4,5,6,7,8,9,10,11,12,13,14,15,16]

{"sheet":"DS","index":44,"n_rows":203,"n_cols":11,"rows_kept":203,"cut":false,"hidden":false,"merged":[[0,0,0,10]]}
["Sample of 200 sales from www.boomerangs.com. Use Descriptive Statistics to reveal patterns in raw data."]
[]
["No.","Date","Coupon","Payment Method","Region","Price","Units","Amount of Transaction (Revenue)","Product (Boomerang Name)","Customer Age","Range (Flight out meters)"]
[1,"2015-05-22","Coupon","Mastercard","West",22.95,2,45.9,"Carlota",21,20]
[2,"2015-06-29","Coupon","Mastercard","West",19.95,1,19.95,"Yanaki",57,25]
[3,"2015-11-05","No Coupon","Mastercard","East",21.95,1,21.95,"Aspen",25,22]
[4,"2015-08-31","No Coupon","American Express","West",19.95,1,19.95,"Yanaki",38,25]
[5,"2015-05-30","Coupon","Visa","West",19.95,4,79.8,"Yanaki",22,25]
[6,"2015-10-05","Coupon","PayPal","South",19.95,4,79.8,"Yanaki",29,25]
[7,"2015-03-22","Coupon","Visa","West",19.95,3,59.849999999999994,"Yanaki",18,25]
[8,"2015-06-14","No Coupon","Discover","West",19.95,7,139.65,"Yanaki",64,25]
[9,"2015-05-09","No Coupon","American Express","West",19.95,6,119.69999999999999,"Yanaki",27,25]
[10,"2015-08-28","No Coupon","Visa","West",21.95,2,43.9,"Aspen",22,22]
[11,"2015-11-30","Coupon","PayPal","Mid West",21.95,2,43.9,"Aspen",39,22]
[12,"2015-09-13","No Coupon","Mastercard","Mid West",19.95,5,99.75,"Yanaki",61,25]
[13,"2015-10-23","No Coupon","Visa","Mid West",22.95,5,114.75,"Carlota",18,20]
[14,"2015-09-19","No Coupon","PayPal","West",22.95,4,91.8,"Carlota",22,20]
[15,"2015-09-20","No Coupon","American Express","Mid West",22.95,5,114.75,"Carlota",52,20]
[16,"2015-01-18","Coupon","Visa","West",22.95,1,22.95,"Carlota",22,20]
[17,"2015-07-28","No Coupon","Discover","West",24.95,5,124.75,"Sunset",18,35]
[18,"2015-06-06","No Coupon","PayPal","South",19.95,1,19.95,"Yanaki",55,25]
[19,"2015-01-30","No Coupon","PayPal","East",21.95,4,87.8,"Aspen",20,22]
[20,"2015-10-09","Coupon","PayPal","Mid West",22.95,1,22.95,"Carlota",42,20]
[21,"2015-07-04","No Coupon","PayPal","South",29.95,10,299.5,"Delicate Arch",30,45]
[22,"2015-11-08","Coupon","Visa","Mid West",19.95,1,19.95,"Yanaki",64,25]
[23,"2015-06-09","Coupon","PayPal","Mid West",19.95,5,99.75,"Yanaki",63,25]
[24,"2015-03-17","Coupon","Mastercard","South",19.95,2,39.9,"Yanaki",41,25]
[25,"2015-04-07","No Coupon","Visa","East",21.95,3,65.849999999999994,"Aspen",21,22]
[26,"2015-01-05","No Coupon","PayPal","East",24.95,1,24.95,"Sunset",63,35]
[27,"2015-07-22","No Coupon","American Express","West",21.95,1,21.95,"Aspen",54,22]
[28,"2015-11-12","No Coupon","American Express","West",19.95,1,19.95,"Yanaki",18,25]
[29,"2015-05-11","No Coupon","Discover","West",22.95,4,91.8,"Carlota",26,20]
[30,"2015-04-14","No Coupon","PayPal","Mid West",21.95,3,65.849999999999994,"Aspen",59,22]
[31,"2015-08-11","No Coupon","Discover","West",19.95,2,39.9,"Yanaki",30,25]
[32,"2015-11-14","No Coupon","Visa","West",21.95,2,43.9,"Aspen",64,22]
[33,"2015-01-21","No Coupon","PayPal","West",21.95,3,65.849999999999994,"Aspen",30,22]
[34,"2015-04-12","Coupon","American Express","West",21.95,2,43.9,"Aspen",41,22]
[35,"2015-06-04","Coupon","Mastercard","South",19.95,5,99.75,"Yanaki",23,25]
[36,"2015-10-19","Coupon","Visa","West",29.95,1,29.95,"Delicate Arch",52,45]
[37,"2015-09-07","No Coupon","PayPal","West",21.95,3,65.849999999999994,"Aspen",18,22]
[38,"2015-09-19","No Coupon","PayPal","West",22.95,1,22.95,"Carlota",32,20]
[39,"2015-01-17","Coupon","American Express","Mid West",19.95,1,19.95,"Yanaki",22,25]
[40,"2015-03-15","Coupon","PayPal","South",19.95,5,99.75,"Yanaki",29,25]
[41,"2015-12-16","No Coupon","Mastercard","Mid West",24.95,5,124.75,"Bellen",25,30]
[42,"2015-07-08","No Coupon","PayPal","West",21.95,1,21.95,"Aspen",63,22]
[43,"2015-05-13","No Coupon","Mastercard","Mid West",24.95,7,174.65,"Sunset",26,35]
[44,"2015-03-03","Coupon","Discover","West",29.95,1,29.95,"Delicate Arch",31,45]
[45,"2015-04-30","No Coupon","PayPal","Mid West",29.95,5,149.75,"Delicate Arch",23,45]
[46,"2015-07-11","Coupon","PayPal","Mid West",29.95,1,29.95,"Delicate Arch",42,45]
[47,"2015-03-22","Coupon","Visa","East",22.95,2,45.9,"Carlota",28,20]
[48,"2015-08-27","No Coupon","Visa","West",22.95,7,160.65,"Carlota",23,20]
[49,"2015-11-16","No Coupon","Mastercard","Mid West",22.95,7,160.65,"Carlota",19,20]
[50,"2015-01-26","Coupon","Visa","West",19.95,6,119.69999999999999,"Yanaki",37,25]
[51,"2015-05-06","No Coupon","Mastercard","East",21.95,2,43.9,"Aspen",38,22]
[52,"2015-03-18","Coupon","Mastercard","Mid West",19.95,6,119.69999999999999,"Yanaki",50,25]
[53,"2015-04-16","No Coupon","Discover","West",21.95,3,65.849999999999994,"Aspen",26,22]
[54,"2015-01-05","No Coupon","PayPal","South",22.95,1,22.95,"Carlota",33,20]
[55,"2015-11-09","No Coupon","PayPal","Mid West",21.95,3,65.849999999999994,"Aspen",59,22]
[56,"2015-07-29","No Coupon","Visa","Mid West",21.95,5,109.75,"Aspen",32,22]
[57,"2015-12-07","No Coupon","Visa","Mid West",22.95,5,114.75,"Carlota",27,20]
[58,"2015-11-22","Coupon","American Express","West",24.95,10,249.5,"Bellen",54,30]
[59,"2015-01-10","No Coupon","PayPal","West",21.95,5,109.75,"Aspen",63,22]
[60,"2015-06-01","No Coupon","Visa","East",22.95,5,114.75,"Carlota",48,20]
[61,"2015-05-02","No Coupon","American Express","East",22.95,5,114.75,"Carlota",29,20]
[62,"2015-06-13","Coupon","Visa","West",29.95,4,119.8,"Delicate Arch",38,45]
[63,"2015-02-16","Coupon","Mastercard","East",29.95,1,29.95,"Delicate Arch",24,45]
[64,"2015-08-02","No Coupon","Visa","West",22.95,5,114.75,"Carlota",51,20]
[65,"2015-11-01","Coupon","Mastercard","West",29.95,7,209.65,"Delicate Arch",25,45]
[66,"2015-11-08","No Coupon","Visa","West",22.95,5,114.75,"Carlota",65,20]
[67,"2015-08-17","No Coupon","PayPal","Mid West",19.95,3,59.849999999999994,"Yanaki",35,25]
[68,"2015-09-27","No Coupon","Mastercard","East",24.95,5,124.75,"Sunset",35,35]
[69,"2015-01-09","No Coupon","Visa","Mid West",19.95,2,39.9,"Yanaki",21,25]
[70,"2015-10-10","No Coupon","Mastercard","West",24.95,5,124.75,"Sunset",47,35]
[71,"2015-12-31","No Coupon","Discover","East",24.95,1,24.95,"Sunset",62,35]
[72,"2015-12-06","No Coupon","PayPal","East",22.95,1,22.95,"Carlota",63,20]
[73,"2015-03-18","No Coupon","American Express","Mid West",24.95,10,249.5,"Bellen",23,30]
[74,"2015-03-22","No Coupon","Visa","South",19.95,3,59.849999999999994,"Yanaki",59,25]
[75,"2015-09-14","Coupon","Visa","West",21.95,2,43.9,"Aspen",48,22]
[76,"2015-02-09","No Coupon","Mastercard","South",19.95,1,19.95,"Yanaki",57,25]
[77,"2015-03-24","No Coupon","Discover","Mid West",29.95,1,29.95,"Delicate Arch",19,45]
[78,"2015-07-28","Coupon","Mastercard","Mid West",19.95,1,19.95,"Yanaki",48,25]
[79,"2015-08-26","Coupon","Mastercard","South",21.95,3,65.849999999999994,"Aspen",21,22]
[80,"2015-03-05","No Coupon","American Express","West",22.95,1,22.95,"Carlota",40,20]
[81,"2015-08-18","No Coupon","Visa","Mid West",19.95,4,79.8,"Yanaki",25,25]
[82,"2015-11-02","Coupon","Mastercard","South",19.95,4,79.8,"Yanaki",59,25]
[83,"2015-12-19","No Coupon","Visa","West",22.95,5,114.75,"Carlota",57,20]
[84,"2015-07-05","Coupon","Discover","West",22.95,1,22.95,"Carlota",56,20]
[85,"2015-04-16","No Coupon","Visa","West",19.95,1,19.95,"Yanaki",19,25]
[86,"2015-12-23","No Coupon","Visa","Mid West",21.95,5,109.75,"Aspen",54,22]
[87,"2015-05-29","No Coupon","PayPal","Mid West",29.95,2,59.9,"Delicate Arch",65,45]
[88,"2015-05-01","Coupon","PayPal","West",21.95,2,43.9,"Aspen",25,22]
[89,"2015-07-03","No Coupon","American Express","West",19.95,2,39.9,"Yanaki",30,25]
[90,"2015-08-12","No Coupon","Mastercard","West",29.95,7,209.65,"Delicate Arch",57,45]
[91,"2015-06-21","Coupon","PayPal","West",19.95,1,19.95,"Yanaki",43,25]
[92,"2015-12-15","No Coupon","Mastercard","South",19.95,4,79.8,"Yanaki",44,25]
[93,"2015-05-25","No Coupon","PayPal","East",24.95,5,124.75,"Bellen",21,30]
[94,"2015-03-23","No Coupon","PayPal","West",24.95,1,24.95,"Bellen",20,30]
[95,"2015-01-22","Coupon","Visa","West",22.95,7,160.65,"Carlota",49,20]
[96,"2015-09-26","No Coupon","Mastercard","South",21.95,3,65.849999999999994,"Aspen",37,22]
[97,"2015-12-27","No Coupon","PayPal","West",22.95,6,137.69999999999999,"Carlota",69,20]
[98,"2015-07-24","No Coupon","Mastercard","East",29.95,1,29.95,"Delicate Arch",62,45]
[99,"2015-04-27","No Coupon","PayPal","West",19.95,6,119.69999999999999,"Yanaki",38,25]
[100,"2015-05-07","No Coupon","Visa","West",19.95,1,19.95,"Yanaki",55,25]
[101,"2015-03-03","Coupon","American Express","East",19.95,2,39.9,"Yanaki",24,25]
[102,"2015-03-23","No Coupon","PayPal","Mid West",22.95,5,114.75,"Carlota",30,20]
[103,"2015-12-05","Coupon","Visa","Mid West",21.95,1,21.95,"Aspen",39,22]
[104,"2015-11-11","Coupon","PayPal","South",29.95,6,179.7,"Delicate Arch",59,45]
[105,"2015-04-13","No Coupon","Discover","East",19.95,2,39.9,"Yanaki",26,25]
[106,"2015-12-13","No Coupon","Visa","Mid West",24.95,1,24.95,"Sunset",33,35]
[107,"2015-10-05","No Coupon","Visa","West",24.95,4,99.8,"Bellen",20,30]
[108,"2015-01-02","No Coupon","Visa","West",21.95,4,87.8,"Aspen",18,22]
[109,"2015-11-04","Coupon","PayPal","East",19.95,2,39.9,"Yanaki",19,25]
[110,"2015-04-29","No Coupon","PayPal","West",21.95,3,65.849999999999994,"Aspen",61,22]
[111,"2015-10-02","Coupon","PayPal","East",19.95,10,199.5,"Yanaki",39,25]
[112,"2015-12-26","Coupon","Discover","West",21.95,1,21.95,"Aspen",52,22]
[113,"2015-11-05","No Coupon","Visa","West",24.95,5,124.75,"Sunset",20,35]
[114,"2015-11-15","No Coupon","Visa","West",29.95,1,29.95,"Delicate Arch",50,45]
[115,"2015-06-27","Coupon","PayPal","South",21.95,1,21.95,"Aspen",29,22]
[116,"2015-04-21","No Coupon","Visa","West",29.95,4,119.8,"Delicate Arch",55,45]
[117,"2015-09-25","No Coupon","American Express","West",29.95,3,89.85,"Delicate Arch",18,45]
[118,"2015-01-24","Coupon","Mastercard","Mid West",21.95,3,65.849999999999994,"Aspen",39,22]
[119,"2015-01-04","No Coupon","Visa","South",22.95,4,91.8,"Carlota",35,20]
[120,"2015-05-06","Coupon","American Express","South",24.95,6,149.69999999999999,"Bellen",38,30]
[121,"2015-06-22","No Coupon","Visa","East",19.95,3,59.849999999999994,"Yanaki",20,25]
[122,"2015-07-12","No Coupon","Discover","Mid West",21.95,6,131.69999999999999,"Aspen",60,22]
[123,"2015-05-03","No Coupon","Visa","Mid West",24.95,2,49.9,"Sunset",61,35]
[124,"2015-03-09","Coupon","PayPal","West",21.95,2,43.9,"Aspen",67,22]
[125,"2015-03-02","No Coupon","Visa","Mid West",19.95,1,19.95,"Yanaki",21,25]
[126,"2015-03-28","No Coupon","Discover","South",21.95,1,21.95,"Aspen",40,22]
[127,"2015-01-15","No Coupon","Visa","West",24.95,1,24.95,"Bellen",25,30]
[128,"2015-11-18","No Coupon","Mastercard","West",19.95,2,39.9,"Yanaki",50,25]
[129,"2015-03-27","No Coupon","Visa","West",24.95,1,24.95,"Bellen",24,30]
[130,"2015-08-11","Coupon","PayPal","West",22.95,5,114.75,"Carlota",28,20]
[131,"2015-12-24","No Coupon","Visa","Mid West",19.95,6,119.69999999999999,"Yanaki",22,25]
[132,"2015-11-27","No Coupon","PayPal","West",21.95,2,43.9,"Aspen",22,22]
[133,"2015-03-12","No Coupon","Visa","West",24.95,1,24.95,"Bellen",27,30]
[134,"2015-09-16","Coupon","Visa","West",24.95,7,174.65,"Sunset",54,35]
[135,"2015-02-01","No Coupon","Mastercard","West",24.95,1,24.95,"Bellen",25,30]
[136,"2015-07-03","No Coupon","Visa","Mid West",24.95,2,49.9,"Sunset",25,35]
[137,"2015-08-30","No Coupon","PayPal","East",19.95,1,19.95,"Yanaki",30,25]
[138,"2015-09-18","Coupon","Mastercard","South",19.95,2,39.9,"Yanaki",59,25]
[139,"2015-11-24","No Coupon","Visa","West",22.95,2,45.9,"Carlota",51,20]
[140,"2015-12-22","No Coupon","Visa","West",29.95,6,179.7,"Delicate Arch",38,45]
[141,"2015-04-23","No Coupon","PayPal","South",22.95,7,160.65,"Carlota",28,20]
[142,"2015-11-18","Coupon","Discover","West",21.95,1,21.95,"Aspen",30,22]
[143,"2015-10-28","No Coupon","Visa","South",24.95,5,124.75,"Bellen",43,30]
[144,"2015-08-14","No Coupon","PayPal","West",29.95,3,89.85,"Delicate Arch",42,45]
[145,"2015-05-05","No Coupon","Visa","Mid West",24.95,2,49.9,"Bellen",24,30]
[146,"2015-08-25","Coupon","Visa","Mid West",22.95,1,22.95,"Carlota",47,20]
[147,"2015-10-25","No Coupon","PayPal","Mid West",19.95,1,19.95,"Yanaki",23,25]
[148,"2015-07-31","No Coupon","PayPal","East",19.95,5,99.75,"Yanaki",59,25]
[149,"2015-07-25","Coupon","Mastercard","West",22.95,4,91.8,"Carlota",22,20]
[150,"2015-09-30","Coupon","Visa","Mid West",24.95,2,49.9,"Bellen",27,30]
[151,"2015-10-03","No Coupon","Discover","East",21.95,1,21.95,"Aspen",65,22]
[152,"2015-04-01","No Coupon","Mastercard","Mid West",22.95,2,45.9,"Carlota",27,20]
[153,"2015-12-15","No Coupon","American Express","East",22.95,1,22.95,"Carlota",27,20]
[154,"2015-01-07","No Coupon","PayPal","West",22.95,2,45.9,"Carlota",28,20]
[155,"2015-07-23","Coupon","PayPal","West",24.95,1,24.95,"Bellen",22,30]
[156,"2015-03-16","No Coupon","PayPal","East",19.95,2,39.9,"Yanaki",42,25]
[157,"2015-12-04","No Coupon","American Express","East",29.95,6,179.7,"Delicate Arch",25,45]
[158,"2015-01-08","Coupon","Mastercard","West",21.95,1,21.95,"Aspen",41,22]
[159,"2015-06-02","Coupon","PayPal","West",24.95,1,24.95,"Bellen",46,30]
[160,"2015-08-18","No Coupon","Discover","West",24.95,3,74.849999999999994,"Sunset",41,35]
[161,"2015-04-24","No Coupon","Mastercard","West",19.95,4,79.8,"Yanaki",29,25]
[162,"2015-12-01","No Coupon","Visa","Mid West",19.95,1,19.95,"Yanaki",50,25]
[163,"2015-12-31","Coupon","Visa","Mid West",22.95,1,22.95,"Carlota",50,20]
[164,"2015-04-30","Coupon","PayPal","Mid West",22.95,2,45.9,"Carlota",34,20]
[165,"2015-03-16","Coupon","American Express","West",21.95,3,65.849999999999994,"Aspen",24,22]
[166,"2015-01-28","No Coupon","Mastercard","West",19.95,7,139.65,"Yanaki",29,25]
[167,"2015-08-03","No Coupon","Mastercard","Mid West",22.95,1,22.95,"Carlota",35,20]
[168,"2015-11-11","No Coupon","Visa","West",19.95,2,39.9,"Yanaki",24,25]
[169,"2015-12-13","Coupon","Visa","South",21.95,5,109.75,"Aspen",23,22]
[170,"2015-03-27","No Coupon","PayPal","Mid West",22.95,7,160.65,"Carlota",31,20]
[171,"2015-10-08","Coupon","PayPal","Mid West",24.95,2,49.9,"Bellen",24,30]
[172,"2015-08-03","No Coupon","Visa","Mid West",22.95,1,22.95,"Carlota",30,20]
[173,"2015-03-10","Coupon","Visa","West",22.95,2,45.9,"Carlota",22,20]
[174,"2015-03-02","No Coupon","Visa","East",22.95,3,68.849999999999994,"Carlota",43,20]
[175,"2015-01-26","No Coupon","Visa","West",22.95,10,229.5,"Carlota",55,20]
[176,"2015-05-06","No Coupon","American Express","West",29.95,1,29.95,"Delicate Arch",38,45]
[177,"2015-03-10","Coupon","Visa","West",19.95,2,39.9,"Yanaki",28,25]
[178,"2015-11-18","Coupon","PayPal","Mid West",21.95,2,43.9,"Aspen",30,22]
[179,"2015-01-30","Coupon","Discover","West",19.95,1,19.95,"Yanaki",35,25]
[180,"2015-07-02","No Coupon","Visa","West",29.95,2,59.9,"Delicate Arch",41,45]
[181,"2015-10-13","Coupon","Discover","East",19.95,5,99.75,"Yanaki",28,25]
[182,"2015-02-07","No Coupon","Discover","Mid West",24.95,7,174.65,"Bellen",65,30]
[183,"2015-06-05","No Coupon","Mastercard","West",21.95,3,65.849999999999994,"Aspen",20,22]
[184,"2015-11-02","No Coupon","Visa","Mid West",24.95,5,124.75,"Sunset",39,35]
[185,"2015-12-13","No Coupon","Visa","East",21.95,4,87.8,"Aspen",19,22]
[186,"2015-10-23","No Coupon","Visa","West",22.95,10,229.5,"Carlota",64,20]
[187,"2015-12-22","Coupon","PayPal","South",19.95,5,99.75,"Yanaki",42,25]
[188,"2015-08-21","Coupon","PayPal","Mid West",19.95,1,19.95,"Yanaki",24,25]
[189,"2015-08-28","Coupon","PayPal","Mid West",19.95,1,19.95,"Yanaki",24,25]
[190,"2015-06-20","No Coupon","American Express","Mid West",24.95,4,99.8,"Bellen",18,30]
[191,"2015-04-30","Coupon","Mastercard","East",24.95,1,24.95,"Bellen",36,30]
[192,"2015-10-14","No Coupon","Mastercard","Mid West",22.95,5,114.75,"Carlota",61,20]
[193,"2015-08-04","No Coupon","Visa","West",19.95,3,59.849999999999994,"Yanaki",22,25]
[194,"2015-10-28","Coupon","PayPal","West",21.95,2,43.9,"Aspen",55,22]
[195,"2015-11-04","No Coupon","American Express","West",24.95,3,74.849999999999994,"Sunset",27,35]
[196,"2015-01-05","No Coupon","PayPal","Mid West",19.95,1,19.95,"Yanaki",49,25]
[197,"2015-12-11","Coupon","Visa","West",22.95,1,22.95,"Carlota",29,20]
[198,"2015-07-18","No Coupon","PayPal","West",21.95,7,153.65,"Aspen",33,22]
[199,"2015-05-31","No Coupon","Visa","West",19.95,2,39.9,"Yanaki",52,25]
[200,"2015-12-16","Coupon","PayPal","West",21.95,6,131.69999999999999,"Aspen",26,22]

{"sheet":"DS (an)","index":45,"n_rows":203,"n_cols":15,"rows_kept":203,"cut":false,"hidden":false,"merged":[[0,0,0,10]]}
["Sample of 200 sales from www.boomerangs.com. Use Descriptive Statistics to reveal patterns in raw data."]
[]
["No.","Date","Coupon","Payment Method","Region","Price","Units","Amount of Transaction (Revenue)","Product (Boomerang Name)","Customer Age","Range (Flight out meters)",null,null,"Amount of Transaction (Revenue)"]
[1,"2015-05-22","Coupon","Mastercard","West",22.95,2,45.9,"Carlota",21,20]
[2,"2015-06-29","Coupon","Mastercard","West",19.95,1,19.95,"Yanaki",57,25,null,null,"Mean",75.057750000000041]
[3,"2015-11-05","No Coupon","Mastercard","East",21.95,1,21.95,"Aspen",25,22,null,null,"Standard Error",3.9247905890668311]
[4,"2015-08-31","No Coupon","American Express","West",19.95,1,19.95,"Yanaki",38,25,null,null,"Median",59.849999999999994]
[5,"2015-05-30","Coupon","Visa","West",19.95,4,79.8,"Yanaki",22,25,null,null,"Mode",19.95]
[6,"2015-10-05","Coupon","PayPal","South",19.95,4,79.8,"Yanaki",29,25,null,null,"Standard Deviation",55.504920805326016]
[7,"2015-03-22","Coupon","Visa","West",19.95,3,59.849999999999994,"Yanaki",18,25,null,null,"Sample Variance",3080.7962336055125]
[8,"2015-06-14","No Coupon","Discover","West",19.95,7,139.65,"Yanaki",64,25,null,null,"Kurtosis",1.3861851155914824]
[9,"2015-05-09","No Coupon","American Express","West",19.95,6,119.69999999999999,"Yanaki",27,25,null,null,"Skewness",1.2141941767956757]
[10,"2015-08-28","No Coupon","Visa","West",21.95,2,43.9,"Aspen",22,22,null,null,"Range",279.55]
[11,"2015-11-30","Coupon","PayPal","Mid West",21.95,2,43.9,"Aspen",39,22,null,null,"Minimum",19.95]
[12,"2015-09-13","No Coupon","Mastercard","Mid West",19.95,5,99.75,"Yanaki",61,25,null,null,"Maximum",299.5]
[13,"2015-10-23","No Coupon","Visa","Mid West",22.95,5,114.75,"Carlota",18,20,null,null,"Sum",15011.550000000008]
[14,"2015-09-19","No Coupon","PayPal","West",22.95,4,91.8,"Carlota",22,20,null,null,"Count",200]
[15,"2015-09-20","No Coupon","American Express","Mid West",22.95,5,114.75,"Carlota",52,20,null,null,"Largest(2)",249.5]
[16,"2015-01-18","Coupon","Visa","West",22.95,1,22.95,"Carlota",22,20,null,null,"Smallest(2)",19.95]
[17,"2015-07-28","No Coupon","Discover","West",24.95,5,124.75,"Sunset",18,35]
[18,"2015-06-06","No Coupon","PayPal","South",19.95,1,19.95,"Yanaki",55,25]
[19,"2015-01-30","No Coupon","PayPal","East",21.95,4,87.8,"Aspen",20,22]
[20,"2015-10-09","Coupon","PayPal","Mid West",22.95,1,22.95,"Carlota",42,20]
[21,"2015-07-04","No Coupon","PayPal","South",29.95,10,299.5,"Delicate Arch",30,45]
[22,"2015-11-08","Coupon","Visa","Mid West",19.95,1,19.95,"Yanaki",64,25]
[23,"2015-06-09","Coupon","PayPal","Mid West",19.95,5,99.75,"Yanaki",63,25]
[24,"2015-03-17","Coupon","Mastercard","South",19.95,2,39.9,"Yanaki",41,25]
[25,"2015-04-07","No Coupon","Visa","East",21.95,3,65.849999999999994,"Aspen",21,22]
[26,"2015-01-05","No Coupon","PayPal","East",24.95,1,24.95,"Sunset",63,35]
[27,"2015-07-22","No Coupon","American Express","West",21.95,1,21.95,"Aspen",54,22]
[28,"2015-11-12","No Coupon","American Express","West",19.95,1,19.95,"Yanaki",18,25]
[29,"2015-05-11","No Coupon","Discover","West",22.95,4,91.8,"Carlota",26,20]
[30,"2015-04-14","No Coupon","PayPal","Mid West",21.95,3,65.849999999999994,"Aspen",59,22]
[31,"2015-08-11","No Coupon","Discover","West",19.95,2,39.9,"Yanaki",30,25]
[32,"2015-11-14","No Coupon","Visa","West",21.95,2,43.9,"Aspen",64,22]
[33,"2015-01-21","No Coupon","PayPal","West",21.95,3,65.849999999999994,"Aspen",30,22]
[34,"2015-04-12","Coupon","American Express","West",21.95,2,43.9,"Aspen",41,22]
[35,"2015-06-04","Coupon","Mastercard","South",19.95,5,99.75,"Yanaki",23,25]
[36,"2015-10-19","Coupon","Visa","West",29.95,1,29.95,"Delicate Arch",52,45]
[37,"2015-09-07","No Coupon","PayPal","West",21.95,3,65.849999999999994,"Aspen",18,22]
[38,"2015-09-19","No Coupon","PayPal","West",22.95,1,22.95,"Carlota",32,20]
[39,"2015-01-17","Coupon","American Express","Mid West",19.95,1,19.95,"Yanaki",22,25]
[40,"2015-03-15","Coupon","PayPal","South",19.95,5,99.75,"Yanaki",29,25]
[41,"2015-12-16","No Coupon","Mastercard","Mid West",24.95,5,124.75,"Bellen",25,30]
[42,"2015-07-08","No Coupon","PayPal","West",21.95,1,21.95,"Aspen",63,22]
[43,"2015-05-13","No Coupon","Mastercard","Mid West",24.95,7,174.65,"Sunset",26,35]
[44,"2015-03-03","Coupon","Discover","West",29.95,1,29.95,"Delicate Arch",31,45]
[45,"2015-04-30","No Coupon","PayPal","Mid West",29.95,5,149.75,"Delicate Arch",23,45]
[46,"2015-07-11","Coupon","PayPal","Mid West",29.95,1,29.95,"Delicate Arch",42,45]
[47,"2015-03-22","Coupon","Visa","East",22.95,2,45.9,"Carlota",28,20]
[48,"2015-08-27","No Coupon","Visa","West",22.95,7,160.65,"Carlota",23,20]
[49,"2015-11-16","No Coupon","Mastercard","Mid West",22.95,7,160.65,"Carlota",19,20]
[50,"2015-01-26","Coupon","Visa","West",19.95,6,119.69999999999999,"Yanaki",37,25]
[51,"2015-05-06","No Coupon","Mastercard","East",21.95,2,43.9,"Aspen",38,22]
[52,"2015-03-18","Coupon","Mastercard","Mid West",19.95,6,119.69999999999999,"Yanaki",50,25]
[53,"2015-04-16","No Coupon","Discover","West",21.95,3,65.849999999999994,"Aspen",26,22]
[54,"2015-01-05","No Coupon","PayPal","South",22.95,1,22.95,"Carlota",33,20]
[55,"2015-11-09","No Coupon","PayPal","Mid West",21.95,3,65.849999999999994,"Aspen",59,22]
[56,"2015-07-29","No Coupon","Visa","Mid West",21.95,5,109.75,"Aspen",32,22]
[57,"2015-12-07","No Coupon","Visa","Mid West",22.95,5,114.75,"Carlota",27,20]
[58,"2015-11-22","Coupon","American Express","West",24.95,10,249.5,"Bellen",54,30]
[59,"2015-01-10","No Coupon","PayPal","West",21.95,5,109.75,"Aspen",63,22]
[60,"2015-06-01","No Coupon","Visa","East",22.95,5,114.75,"Carlota",48,20]
[61,"2015-05-02","No Coupon","American Express","East",22.95,5,114.75,"Carlota",29,20]
[62,"2015-06-13","Coupon","Visa","West",29.95,4,119.8,"Delicate Arch",38,45]
[63,"2015-02-16","Coupon","Mastercard","East",29.95,1,29.95,"Delicate Arch",24,45]
[64,"2015-08-02","No Coupon","Visa","West",22.95,5,114.75,"Carlota",51,20]
[65,"2015-11-01","Coupon","Mastercard","West",29.95,7,209.65,"Delicate Arch",25,45]
[66,"2015-11-08","No Coupon","Visa","West",22.95,5,114.75,"Carlota",65,20]
[67,"2015-08-17","No Coupon","PayPal","Mid West",19.95,3,59.849999999999994,"Yanaki",35,25]
[68,"2015-09-27","No Coupon","Mastercard","East",24.95,5,124.75,"Sunset",35,35]
[69,"2015-01-09","No Coupon","Visa","Mid West",19.95,2,39.9,"Yanaki",21,25]
[70,"2015-10-10","No Coupon","Mastercard","West",24.95,5,124.75,"Sunset",47,35]
[71,"2015-12-31","No Coupon","Discover","East",24.95,1,24.95,"Sunset",62,35]
[72,"2015-12-06","No Coupon","PayPal","East",22.95,1,22.95,"Carlota",63,20]
[73,"2015-03-18","No Coupon","American Express","Mid West",24.95,10,249.5,"Bellen",23,30]
[74,"2015-03-22","No Coupon","Visa","South",19.95,3,59.849999999999994,"Yanaki",59,25]
[75,"2015-09-14","Coupon","Visa","West",21.95,2,43.9,"Aspen",48,22]
[76,"2015-02-09","No Coupon","Mastercard","South",19.95,1,19.95,"Yanaki",57,25]
[77,"2015-03-24","No Coupon","Discover","Mid West",29.95,1,29.95,"Delicate Arch",19,45]
[78,"2015-07-28","Coupon","Mastercard","Mid West",19.95,1,19.95,"Yanaki",48,25]
[79,"2015-08-26","Coupon","Mastercard","South",21.95,3,65.849999999999994,"Aspen",21,22]
[80,"2015-03-05","No Coupon","American Express","West",22.95,1,22.95,"Carlota",40,20]
[81,"2015-08-18","No Coupon","Visa","Mid West",19.95,4,79.8,"Yanaki",25,25]
[82,"2015-11-02","Coupon","Mastercard","South",19.95,4,79.8,"Yanaki",59,25]
[83,"2015-12-19","No Coupon","Visa","West",22.95,5,114.75,"Carlota",57,20]
[84,"2015-07-05","Coupon","Discover","West",22.95,1,22.95,"Carlota",56,20]
[85,"2015-04-16","No Coupon","Visa","West",19.95,1,19.95,"Yanaki",19,25]
[86,"2015-12-23","No Coupon","Visa","Mid West",21.95,5,109.75,"Aspen",54,22]
[87,"2015-05-29","No Coupon","PayPal","Mid West",29.95,2,59.9,"Delicate Arch",65,45]
[88,"2015-05-01","Coupon","PayPal","West",21.95,2,43.9,"Aspen",25,22]
[89,"2015-07-03","No Coupon","American Express","West",19.95,2,39.9,"Yanaki",30,25]
[90,"2015-08-12","No Coupon","Mastercard","West",29.95,7,209.65,"Delicate Arch",57,45]
[91,"2015-06-21","Coupon","PayPal","West",19.95,1,19.95,"Yanaki",43,25]
[92,"2015-12-15","No Coupon","Mastercard","South",19.95,4,79.8,"Yanaki",44,25]
[93,"2015-05-25","No Coupon","PayPal","East",24.95,5,124.75,"Bellen",21,30]
[94,"2015-03-23","No Coupon","PayPal","West",24.95,1,24.95,"Bellen",20,30]
[95,"2015-01-22","Coupon","Visa","West",22.95,7,160.65,"Carlota",49,20]
[96,"2015-09-26","No Coupon","Mastercard","South",21.95,3,65.849999999999994,"Aspen",37,22]
[97,"2015-12-27","No Coupon","PayPal","West",22.95,6,137.69999999999999,"Carlota",69,20]
[98,"2015-07-24","No Coupon","Mastercard","East",29.95,1,29.95,"Delicate Arch",62,45]
[99,"2015-04-27","No Coupon","PayPal","West",19.95,6,119.69999999999999,"Yanaki",38,25]
[100,"2015-05-07","No Coupon","Visa","West",19.95,1,19.95,"Yanaki",55,25]
[101,"2015-03-03","Coupon","American Express","East",19.95,2,39.9,"Yanaki",24,25]
[102,"2015-03-23","No Coupon","PayPal","Mid West",22.95,5,114.75,"Carlota",30,20]
[103,"2015-12-05","Coupon","Visa","Mid West",21.95,1,21.95,"Aspen",39,22]
[104,"2015-11-11","Coupon","PayPal","South",29.95,6,179.7,"Delicate Arch",59,45]
[105,"2015-04-13","No Coupon","Discover","East",19.95,2,39.9,"Yanaki",26,25]
[106,"2015-12-13","No Coupon","Visa","Mid West",24.95,1,24.95,"Sunset",33,35]
[107,"2015-10-05","No Coupon","Visa","West",24.95,4,99.8,"Bellen",20,30]
[108,"2015-01-02","No Coupon","Visa","West",21.95,4,87.8,"Aspen",18,22]
[109,"2015-11-04","Coupon","PayPal","East",19.95,2,39.9,"Yanaki",19,25]
[110,"2015-04-29","No Coupon","PayPal","West",21.95,3,65.849999999999994,"Aspen",61,22]
[111,"2015-10-02","Coupon","PayPal","East",19.95,10,199.5,"Yanaki",39,25]
[112,"2015-12-26","Coupon","Discover","West",21.95,1,21.95,"Aspen",52,22]
[113,"2015-11-05","No Coupon","Visa","West",24.95,5,124.75,"Sunset",20,35]
[114,"2015-11-15","No Coupon","Visa","West",29.95,1,29.95,"Delicate Arch",50,45]
[115,"2015-06-27","Coupon","PayPal","South",21.95,1,21.95,"Aspen",29,22]
[116,"2015-04-21","No Coupon","Visa","West",29.95,4,119.8,"Delicate Arch",55,45]
[117,"2015-09-25","No Coupon","American Express","West",29.95,3,89.85,"Delicate Arch",18,45]
[118,"2015-01-24","Coupon","Mastercard","Mid West",21.95,3,65.849999999999994,"Aspen",39,22]
[119,"2015-01-04","No Coupon","Visa","South",22.95,4,91.8,"Carlota",35,20]
[120,"2015-05-06","Coupon","American Express","South",24.95,6,149.69999999999999,"Bellen",38,30]
[121,"2015-06-22","No Coupon","Visa","East",19.95,3,59.849999999999994,"Yanaki",20,25]
[122,"2015-07-12","No Coupon","Discover","Mid West",21.95,6,131.69999999999999,"Aspen",60,22]
[123,"2015-05-03","No Coupon","Visa","Mid West",24.95,2,49.9,"Sunset",61,35]
[124,"2015-03-09","Coupon","PayPal","West",21.95,2,43.9,"Aspen",67,22]
[125,"2015-03-02","No Coupon","Visa","Mid West",19.95,1,19.95,"Yanaki",21,25]
[126,"2015-03-28","No Coupon","Discover","South",21.95,1,21.95,"Aspen",40,22]
[127,"2015-01-15","No Coupon","Visa","West",24.95,1,24.95,"Bellen",25,30]
[128,"2015-11-18","No Coupon","Mastercard","West",19.95,2,39.9,"Yanaki",50,25]
[129,"2015-03-27","No Coupon","Visa","West",24.95,1,24.95,"Bellen",24,30]
[130,"2015-08-11","Coupon","PayPal","West",22.95,5,114.75,"Carlota",28,20]
[131,"2015-12-24","No Coupon","Visa","Mid West",19.95,6,119.69999999999999,"Yanaki",22,25]
[132,"2015-11-27","No Coupon","PayPal","West",21.95,2,43.9,"Aspen",22,22]
[133,"2015-03-12","No Coupon","Visa","West",24.95,1,24.95,"Bellen",27,30]
[134,"2015-09-16","Coupon","Visa","West",24.95,7,174.65,"Sunset",54,35]
[135,"2015-02-01","No Coupon","Mastercard","West",24.95,1,24.95,"Bellen",25,30]
[136,"2015-07-03","No Coupon","Visa","Mid West",24.95,2,49.9,"Sunset",25,35]
[137,"2015-08-30","No Coupon","PayPal","East",19.95,1,19.95,"Yanaki",30,25]
[138,"2015-09-18","Coupon","Mastercard","South",19.95,2,39.9,"Yanaki",59,25]
[139,"2015-11-24","No Coupon","Visa","West",22.95,2,45.9,"Carlota",51,20]
[140,"2015-12-22","No Coupon","Visa","West",29.95,6,179.7,"Delicate Arch",38,45]
[141,"2015-04-23","No Coupon","PayPal","South",22.95,7,160.65,"Carlota",28,20]
[142,"2015-11-18","Coupon","Discover","West",21.95,1,21.95,"Aspen",30,22]
[143,"2015-10-28","No Coupon","Visa","South",24.95,5,124.75,"Bellen",43,30]
[144,"2015-08-14","No Coupon","PayPal","West",29.95,3,89.85,"Delicate Arch",42,45]
[145,"2015-05-05","No Coupon","Visa","Mid West",24.95,2,49.9,"Bellen",24,30]
[146,"2015-08-25","Coupon","Visa","Mid West",22.95,1,22.95,"Carlota",47,20]
[147,"2015-10-25","No Coupon","PayPal","Mid West",19.95,1,19.95,"Yanaki",23,25]
[148,"2015-07-31","No Coupon","PayPal","East",19.95,5,99.75,"Yanaki",59,25]
[149,"2015-07-25","Coupon","Mastercard","West",22.95,4,91.8,"Carlota",22,20]
[150,"2015-09-30","Coupon","Visa","Mid West",24.95,2,49.9,"Bellen",27,30]
[151,"2015-10-03","No Coupon","Discover","East",21.95,1,21.95,"Aspen",65,22]
[152,"2015-04-01","No Coupon","Mastercard","Mid West",22.95,2,45.9,"Carlota",27,20]
[153,"2015-12-15","No Coupon","American Express","East",22.95,1,22.95,"Carlota",27,20]
[154,"2015-01-07","No Coupon","PayPal","West",22.95,2,45.9,"Carlota",28,20]
[155,"2015-07-23","Coupon","PayPal","West",24.95,1,24.95,"Bellen",22,30]
[156,"2015-03-16","No Coupon","PayPal","East",19.95,2,39.9,"Yanaki",42,25]
[157,"2015-12-04","No Coupon","American Express","East",29.95,6,179.7,"Delicate Arch",25,45]
[158,"2015-01-08","Coupon","Mastercard","West",21.95,1,21.95,"Aspen",41,22]
[159,"2015-06-02","Coupon","PayPal","West",24.95,1,24.95,"Bellen",46,30]
[160,"2015-08-18","No Coupon","Discover","West",24.95,3,74.849999999999994,"Sunset",41,35]
[161,"2015-04-24","No Coupon","Mastercard","West",19.95,4,79.8,"Yanaki",29,25]
[162,"2015-12-01","No Coupon","Visa","Mid West",19.95,1,19.95,"Yanaki",50,25]
[163,"2015-12-31","Coupon","Visa","Mid West",22.95,1,22.95,"Carlota",50,20]
[164,"2015-04-30","Coupon","PayPal","Mid West",22.95,2,45.9,"Carlota",34,20]
[165,"2015-03-16","Coupon","American Express","West",21.95,3,65.849999999999994,"Aspen",24,22]
[166,"2015-01-28","No Coupon","Mastercard","West",19.95,7,139.65,"Yanaki",29,25]
[167,"2015-08-03","No Coupon","Mastercard","Mid West",22.95,1,22.95,"Carlota",35,20]
[168,"2015-11-11","No Coupon","Visa","West",19.95,2,39.9,"Yanaki",24,25]
[169,"2015-12-13","Coupon","Visa","South",21.95,5,109.75,"Aspen",23,22]
[170,"2015-03-27","No Coupon","PayPal","Mid West",22.95,7,160.65,"Carlota",31,20]
[171,"2015-10-08","Coupon","PayPal","Mid West",24.95,2,49.9,"Bellen",24,30]
[172,"2015-08-03","No Coupon","Visa","Mid West",22.95,1,22.95,"Carlota",30,20]
[173,"2015-03-10","Coupon","Visa","West",22.95,2,45.9,"Carlota",22,20]
[174,"2015-03-02","No Coupon","Visa","East",22.95,3,68.849999999999994,"Carlota",43,20]
[175,"2015-01-26","No Coupon","Visa","West",22.95,10,229.5,"Carlota",55,20]
[176,"2015-05-06","No Coupon","American Express","West",29.95,1,29.95,"Delicate Arch",38,45]
[177,"2015-03-10","Coupon","Visa","West",19.95,2,39.9,"Yanaki",28,25]
[178,"2015-11-18","Coupon","PayPal","Mid West",21.95,2,43.9,"Aspen",30,22]
[179,"2015-01-30","Coupon","Discover","West",19.95,1,19.95,"Yanaki",35,25]
[180,"2015-07-02","No Coupon","Visa","West",29.95,2,59.9,"Delicate Arch",41,45]
[181,"2015-10-13","Coupon","Discover","East",19.95,5,99.75,"Yanaki",28,25]
[182,"2015-02-07","No Coupon","Discover","Mid West",24.95,7,174.65,"Bellen",65,30]
[183,"2015-06-05","No Coupon","Mastercard","West",21.95,3,65.849999999999994,"Aspen",20,22]
[184,"2015-11-02","No Coupon","Visa","Mid West",24.95,5,124.75,"Sunset",39,35]
[185,"2015-12-13","No Coupon","Visa","East",21.95,4,87.8,"Aspen",19,22]
[186,"2015-10-23","No Coupon","Visa","West",22.95,10,229.5,"Carlota",64,20]
[187,"2015-12-22","Coupon","PayPal","South",19.95,5,99.75,"Yanaki",42,25]
[188,"2015-08-21","Coupon","PayPal","Mid West",19.95,1,19.95,"Yanaki",24,25]
[189,"2015-08-28","Coupon","PayPal","Mid West",19.95,1,19.95,"Yanaki",24,25]
[190,"2015-06-20","No Coupon","American Express","Mid West",24.95,4,99.8,"Bellen",18,30]
[191,"2015-04-30","Coupon","Mastercard","East",24.95,1,24.95,"Bellen",36,30]
[192,"2015-10-14","No Coupon","Mastercard","Mid West",22.95,5,114.75,"Carlota",61,20]
[193,"2015-08-04","No Coupon","Visa","West",19.95,3,59.849999999999994,"Yanaki",22,25]
[194,"2015-10-28","Coupon","PayPal","West",21.95,2,43.9,"Aspen",55,22]
[195,"2015-11-04","No Coupon","American Express","West",24.95,3,74.849999999999994,"Sunset",27,35]
[196,"2015-01-05","No Coupon","PayPal","Mid West",19.95,1,19.95,"Yanaki",49,25]
[197,"2015-12-11","Coupon","Visa","West",22.95,1,22.95,"Carlota",29,20]
[198,"2015-07-18","No Coupon","PayPal","West",21.95,7,153.65,"Aspen",33,22]
[199,"2015-05-31","No Coupon","Visa","West",19.95,2,39.9,"Yanaki",52,25]
[200,"2015-12-16","Coupon","PayPal","West",21.95,6,131.69999999999999,"Aspen",26,22]

{"sheet":"Skew (2)","index":46,"n_rows":44,"n_cols":11,"rows_kept":44,"cut":false,"hidden":false,"merged":[]}
[null,"Skew tells to which way the distribution is tilting. If the average is being pulled up by a few big values, the skew is positive, or to the right. If the average is being pulled down by a few small values, the skew is negative, or to the left."]
[null,"If the Skew is 0, the Mean = Median = Mode = Normal or Bell Distribution"]
[null,"We can use the SKEW function to calculate the Skew"]
[]
[]
[null,null,null,null,null,null,null,"Xbar"," =","Sample Mean","\"Xbar\""]
[null,null,null,null,null,null,null,"xi"," =","Particular Value","\"x sub i\""]
[null,null,null,null,null,null,null,"∑"," =","Greek Letter used for \"adding\"","\"Sigma\""]
[null,null,null,null,null,null,null,"n"," =","Count of sample items","\"n\""]
[null,null,null,null,null,null,null,"s"," =","Sample Standard Deviation","\"s\""]
[null,"Moderate Skew","No Skew","Highly Skewed"]
[null,"Negative (Left) Skew\nsmall values pull Mean down","No Skew (Normal or Bell)\nMean = Median = Mode","Positive (Right) Skew\nbig values pull Mean up"]
["count = n",{"$f":"COUNT(B21:B30)","v":10},{"$f":"COUNT(C21:C30)","v":10},{"$f":"COUNT(D21:D30)","v":10}]
["Skew",{"$f":"B13/((B13-1)*(B13-2))*SUMPRODUCT(((B21:B30-AVERAGE(B21:B30))/_xlfn.STDEV.S(B21:B30))^3)","v":-0.64052176797398908},{"$f":"C13/((C13-1)*(C13-2))*SUMPRODUCT(((C21:C30-AVERAGE(C21:C30))/_xlfn.STDEV.S(C21:C30))^3)","v":-1.1277285554160596E-2},{"$f":"D13/((D13-1)*(D13-2))*SUMPRODUCT(((D21:D30-AVERAGE(D21:D30))/_xlfn.STDEV.S(D21:D30))^3)","v":1.5524906262931217}]
["Skew"]
["Mean",{"$f":"AVERAGE(B21:B30)","v":32.6},{"$f":"AVERAGE(C21:C30)","v":24.59},14.39]
["Median",{"$f":"MEDIAN(B21:B30)","v":34.5},{"$f":"MEDIAN(C21:C30)","v":25},9]
["Mode",{"$f":"_xlfn.MODE.SNGL(B21:B30)","v":43},{"$f":"_xlfn.MODE.SNGL(C21:C30)","v":25},7]
[null,"Moderate Skew","No Skew","Highly Skewed"]
[null,"Data 1","Data 2","Data 3"]
[null,7,7.9,1]
[null,43,15,7]
[null,33,11,7]
[null,21,21,12]
[null,15,25,11]
[null,31,25,14]
[null,36,27,2]
[null,43,35,1.9]
[null,48,38,39]
[null,49,41,49]
[]
["Labels","Frequency","Frequency","Frequency"]
[{"$f":"A40&\"-\"&B40-1","v":"0-9"},{"$f":"FREQUENCY(B21:B30,B40:B44)","v":1},{"$f":"FREQUENCY(C21:C30,C40:C44)","v":1},{"$f":"FREQUENCY(D21:D30,D40:D44)","v":5}]
[{"$f":"A41&\"-\"&B41-1","v":"10-19"},1,2,3]
[{"$f":"A42&\"-\"&B42-1","v":"20-29"},1,4,0]
[{"$f":"A43&\"-\"&B43-1","v":"30-39"},3,2,1]
[{"$f":"A44&\"-\"&B44-1","v":"40-49"},4,1,1]
[]
["Low","Bins","Bins","Bins"]
[{"$f":"B40-10","v":0},10,10,10]
[{"$f":"B41-10","v":10},20,20,20]
[{"$f":"B42-10","v":20},30,30,30]
[{"$f":"B43-10","v":30},40,40,40]
[40,50,50,50]

{"sheet":"Skew (2an)","index":47,"n_rows":44,"n_cols":11,"rows_kept":44,"cut":false,"hidden":false,"merged":[]}
[null,"Skew tells to which way the distribution is tilting. If the average is being pulled up by a few big values, the skew is positive, or to the right. If the average is being pulled down by a few small values, the skew is negative, or to the left."]
[null,"If the Skew is 0, the Mean = Median = Mode = Normal or Bell Distribution"]
[null,"We can use the SKEW function to calculate the Skew"]
[]
[]
[null,null,null,null,null,null,null,"Xbar"," =","Sample Mean","\"Xbar\""]
[null,null,null,null,null,null,null,"xi"," =","Particular Value","\"x sub i\""]
[null,null,null,null,null,null,null,"∑"," =","Greek Letter used for \"adding\"","\"Sigma\""]
[null,null,null,null,null,null,null,"n"," =","Count of sample items","\"n\""]
[null,null,null,null,null,null,null,"s"," =","Sample Standard Deviation","\"s\""]
[null,"Moderate Skew","No Skew","Highly Skewed"]
[null,"Negative (Left) Skew\nsmall values pull Mean down","No Skew (Normal or Bell)\nMean = Median = Mode","Positive (Right) Skew\nbig values pull Mean up"]
["count = n",{"$f":"COUNT(B21:B30)","v":10},{"$f":"COUNT(C21:C30)","v":10},{"$f":"COUNT(D21:D30)","v":10}]
["Skew",{"$f":"B13/((B13-1)*(B13-2))*SUMPRODUCT(((B21:B30-AVERAGE(B21:B30))/_xlfn.STDEV.S(B21:B30))^3)","v":-0.64052176797398908},{"$f":"C13/((C13-1)*(C13-2))*SUMPRODUCT(((C21:C30-AVERAGE(C21:C30))/_xlfn.STDEV.S(C21:C30))^3)","v":-1.1277285554160596E-2},{"$f":"D13/((D13-1)*(D13-2))*SUMPRODUCT(((D21:D30-AVERAGE(D21:D30))/_xlfn.STDEV.S(D21:D30))^3)","v":1.5524906262931217}]
["Skew",{"$f":"SKEW(B21:B30)","v":-0.64052176797398896},{"$f":"SKEW(C21:C30)","v":-1.1277285554160533E-2},1.5524906262931217]
["Mean",{"$f":"AVERAGE(B21:B30)","v":32.6},{"$f":"AVERAGE(C21:C30)","v":24.59},14.39]
["Median",{"$f":"MEDIAN(B21:B30)","v":34.5},{"$f":"MEDIAN(C21:C30)","v":25},9]
["Mode",{"$f":"_xlfn.MODE.SNGL(B21:B30)","v":43},{"$f":"_xlfn.MODE.SNGL(C21:C30)","v":25},7]
[null,"Moderate Skew","No Skew","Highly Skewed"]
[null,"Data 1","Data 2","Data 3"]
[null,7,7.9,1]
[null,43,15,7]
[null,33,11,7]
[null,21,21,12]
[null,15,25,11]
[null,31,25,14]
[null,36,27,2]
[null,43,35,1.9]
[null,48,38,39]
[null,49,41,49]
[]
["Labels","Frequency","Frequency","Frequency"]
[{"$f":"A40&\"-\"&B40-1","v":"0-9"},{"$f":"FREQUENCY(B21:B30,B40:B44)","v":1},{"$f":"FREQUENCY(C21:C30,C40:C44)","v":1},{"$f":"FREQUENCY(D21:D30,D40:D44)","v":5}]
[{"$f":"A41&\"-\"&B41-1","v":"10-19"},1,2,3]
[{"$f":"A42&\"-\"&B42-1","v":"20-29"},1,4,0]
[{"$f":"A43&\"-\"&B43-1","v":"30-39"},3,2,1]
[{"$f":"A44&\"-\"&B44-1","v":"40-49"},4,1,1]
[]
["Low","Bins","Bins","Bins"]
[{"$f":"B40-10","v":0},10,10,10]
[{"$f":"B41-10","v":10},20,20,20]
[{"$f":"B42-10","v":20},30,30,30]
[{"$f":"B43-10","v":30},40,40,40]
[40,50,50,50]

{"sheet":"Skew (3)","index":48,"n_rows":0,"n_cols":0,"rows_kept":0,"cut":false,"hidden":false,"merged":[]}

{"sheet":"S(L)","index":49,"n_rows":93,"n_cols":15,"rows_kept":93,"cut":false,"hidden":false,"merged":[]}
["For Quantitative Data, Create Frequency Distribution and Histogram",null,null,null,null,null,null,null,null,null,null,null,null,null,"Other Examples of Skew Left/Negative:"]
[null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Death Rates"]
[]
[null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Skew"]
[null,null,null,null,null,null,null,null,null,null,null,null,null,null,{"$f":"SKEW(A7:A47)","v":-1.3880519856192466}]
["Tests Scores often Skewed Left - a few small values will tend to pull the Average (Mean) down",null,"COUNT function = No. in Sample = n =",{"$f":"COUNT(A7:A47)","v":41}]
[{"$f":"IF(MOD(ROW(),4),RANDBETWEEN(65,100),RANDBETWEEN(0,100))","v":82},null,"MAX function",{"$f":"MAX(A7:A47)","v":98}]
[1,null,"MIN function",{"$f":"MIN(A7:A47)","v":1}]
[88,null,{"$f":"\"Range = (MAX - MIN) = \"&D7&\" - \"&D8&\" =\"","v":"Range = (MAX - MIN) = 98 - 1 ="},{"$f":"D7-D8","v":97}]
[69,null,"# of Classes = k =",7,"This is a judgment call (the art side of statistics)"]
[94,null,{"$f":"\"Is 2^\"&D10&\" (\"&2^D10&\") >= \"&D6","v":"Is 2^7 (128) >= 41"},{"$f":"2^D10>=D6","v":true}]
[60,null,{"$f":"\"Approximate Class Width = Range/k = \"&D9&\"/\"&D10&\" =\"","v":"Approximate Class Width = Range/k = 97/7 ="},{"$f":"(D7-D8)/D10","v":13.857142857142858}]
[69,null,"Class width = ",15,"This is a judgment call (the art side of statistics)"]
[90,null,{"$f":"\"Actual Range for Classes = ClassWidth*k = \"&D13&\"*\"&D10&\" =\"","v":"Actual Range for Classes = ClassWidth*k = 15*7 ="},{"$f":"D13*D10","v":105}]
[84,null,"Lower Limit of First Class (look at MIN then estimate)",0,"This is a judgment call (the art side of statistics)"]
[89,null,null,null,null,null,null,null,null,null,null,null,null,null,"Skew"]
[71,null,null,null,null,"Class","Frequency","% Frequency",null,null,null,null,null,null,{"$f":"SKEW(A53:A93)","v":7.6073693075467061E-2}]
[75,null,null,{"$f":"D15","v":0},{"$f":"D18+D$13","v":15},{"$f":"D18&\" up to \"&E18","v":"0 up to 15"},{"$f":"COUNTIF($A$7:$A$47,\"<\"&E18)-COUNTIF($A$7:$A$47,\"<\"&D18)","v":5},{"$f":"G18/G$25","v":0.12195121951219512}]
[95,null,null,{"$f":"E18","v":15},30,"15 up to 30",0,0]
[30,null,null,30,45,"30 up to 45",2,4.878048780487805E-2]
[68,null,null,45,60,"45 up to 60",0,0]
[78,null,null,60,75,"60 up to 75",12,0.29268292682926828]
[97,null,null,75,90,"75 up to 90",12,0.29268292682926828]
[5,null,null,90,105,"90 up to 105",10,0.24390243902439024]
[75,null,null,null,null,null,{"$f":"SUM(G18:G24)","v":41},{"$f":"SUM(H18:H24)","v":1}]
[69]
[70]
[35]
[76]
[75]
[77]
[14]
[74]
[91]
[91]
[97]
[68]
[66]
[76]
[3]
[66]
[98]
[68]
[10]
[95]
[95]
[83]
[]
[]
[]
[]
["Tests Scores can be bi-modal",null,"COUNT function = No. in Sample = n =",{"$f":"COUNT(A53:A93)","v":41}]
[{"$f":"CHOOSE(RANDBETWEEN(1,5),RANDBETWEEN(0,30),RANDBETWEEN(0,30),RANDBETWEEN(60,100),RANDBETWEEN(60,100),RANDBETWEEN(0,100))","v":22},null,"MAX function",{"$f":"MAX(A53:A93)","v":98}]
[27,null,"MIN function",{"$f":"MIN(A53:A93)","v":1}]
[28,null,{"$f":"\"Range = (MAX - MIN) = \"&D53&\" - \"&D54&\" =\"","v":"Range = (MAX - MIN) = 98 - 1 ="},{"$f":"D53-D54","v":97}]
[60,null,"# of Classes = k =",7,"This is a judgment call (the art side of statistics)"]
[7,null,{"$f":"\"Is 2^\"&D56&\" (\"&2^D56&\") >= \"&D52","v":"Is 2^7 (128) >= 41"},{"$f":"2^D56>=D52","v":true}]
[23,null,{"$f":"\"Approximate Class Width = Range/k = \"&D55&\"/\"&D56&\" =\"","v":"Approximate Class Width = Range/k = 97/7 ="},{"$f":"(D53-D54)/D56","v":13.857142857142858}]
[11,null,"Class width = ",15,"This is a judgment call (the art side of statistics)"]
[1,null,{"$f":"\"Actual Range for Classes = ClassWidth*k = \"&D59&\"*\"&D56&\" =\"","v":"Actual Range for Classes = ClassWidth*k = 15*7 ="},{"$f":"D59*D56","v":105}]
[1,null,"Lower Limit of First Class (look at MIN then estimate)",0,"This is a judgment call (the art side of statistics)"]
[14]
[61,null,null,null,null,"Class","Frequency","% Frequency"]
[80,null,null,{"$f":"D61","v":0},{"$f":"D64+D$59","v":15},{"$f":"D64&\" up to \"&E64","v":"0 up to 15"},{"$f":"COUNTIF($A$53:$A$93,\"<\"&E64)-COUNTIF($A$53:$A$93,\"<\"&D64)","v":8},{"$f":"G64/G$25","v":0.1951219512195122}]
[60,null,null,{"$f":"E64","v":15},30,"15 up to 30",12,0.29268292682926828]
[86,null,null,30,45,"30 up to 45",1,2.4390243902439025E-2]
[24,null,null,45,60,"45 up to 60",0,0]
[80,null,null,60,75,"60 up to 75",10,0.24390243902439024]
[3,null,null,75,90,"75 up to 90",8,0.1951219512195122]
[69,null,null,90,105,"90 up to 105",2,4.878048780487805E-2]
[95,null,null,null,null,null,{"$f":"SUM(G64:G70)","v":41},{"$f":"SUM(H64:H70)","v":1}]
[19]
[82]
[23]
[65]
[84]
[72]
[98]
[9]
[65]
[71]
[74]
[77]
[31]
[21]
[85]
[24]
[80]
[24]
[4]
[25]
[29]
[67]

{"sheet":"S(B)","index":50,"n_rows":47,"n_cols":18,"rows_kept":47,"cut":false,"hidden":false,"merged":[]}
["For Quantitative Data, Create Frequency Distribution and Histogram",null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Notes for Bell Shaped Curve:"]
[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Few population distributions have a Bell Shape, but some are close:"]
[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Human height & weight"]
[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Cereal box weight"]
[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"SAT scores"]
["Human Height often Close to Symmetrical - bell shape - high in the middle and tapering off in both directions",null,"COUNT function = No. in Sample = n =",{"$f":"COUNT(A7:A47)","v":41}]
[{"$f":"IF(MOD(ROW(),4),RANDBETWEEN(450,650)/100,RANDBETWEEN(200,820)/100)","v":5.94},null,"MAX function",{"$f":"MAX(A7:A47)","v":7.88},null,null,null,null,null,null,null,null,null,null,null,null,null,"Skew"]
[7.32,null,"MIN function",{"$f":"MIN(A7:A47)","v":2.38},null,null,null,null,null,null,null,null,null,null,null,null,null,{"$f":"SKEW(A7:A47)","v":-0.94497610277041555}]
[6.31,null,{"$f":"\"Range = (MAX - MIN) = \"&D7&\" - \"&D8&\" =\"","v":"Range = (MAX - MIN) = 7.88 - 2.38 ="},{"$f":"D7-D8","v":5.5}]
[5.48,null,"# of Classes = k =",7,"This is a judgment call (the art side of statistics)"]
[6.03,null,{"$f":"\"Is 2^\"&D10&\" (\"&2^D10&\") >= \"&D6","v":"Is 2^7 (128) >= 41"},{"$f":"2^D10>=D6","v":true}]
[6.74,null,{"$f":"\"Approximate Class Width = Range/k = \"&D9&\"/\"&D10&\" =\"","v":"Approximate Class Width = Range/k = 5.5/7 ="},{"$f":"(D7-D8)/D10","v":0.7857142857142857}]
[5.99,null,"Class width = ",1,"This is a judgment call (the art side of statistics)"]
[6.29,null,{"$f":"\"Actual Range for Classes = ClassWidth*k = \"&D13&\"*\"&D10&\" =\"","v":"Actual Range for Classes = ClassWidth*k = 1*7 ="},{"$f":"D13*D10","v":7}]
[6.26,null,"Lower Limit of First Class (look at MIN then estimate)",2,"This is a judgment call (the art side of statistics)"]
[4.5599999999999996]
[4.5,null,null,null,null,"Class","Frequency","% Frequency"]
[5.45,null,null,{"$f":"D15","v":2},{"$f":"D18+D$13","v":3},{"$f":"D18&\" up to \"&E18","v":"2 up to 3"},{"$f":"COUNTIF($A$7:$A$47,\"<\"&E18)-COUNTIF($A$7:$A$47,\"<\"&D18)","v":1},{"$f":"G18/G$25","v":2.4390243902439025E-2}]
[5.9,null,null,{"$f":"E18","v":3},4,"3 up to 4",2,4.878048780487805E-2]
[4.9800000000000004,null,null,4,5,"4 up to 5",5,0.12195121951219512]
[5.35,null,null,5,6,"5 up to 6",20,0.48780487804878048]
[5.9,null,null,6,7,"6 up to 7",11,0.26829268292682928]
[5.72,null,null,7,8,"7 up to 8",2,4.878048780487805E-2]
[6.85,null,null,8,9,"8 up to 9",0,0]
[5.62,null,null,null,null,null,{"$f":"SUM(G18:G24)","v":41},{"$f":"SUM(H18:H24)","v":1}]
[4.79]
[4.91]
[3.46]
[5.05]
[5.9]
[5.72]
[5.42]
[5.96]
[5.58]
[6.22]
[2.38]
[6.28]
[6.49]
[5.34]
[3.31]
[5.41]
[5.39]
[5.09]
[7.88]
[6.28]
[6.06]
[5.78]

{"sheet":"S(R)","index":51,"n_rows":47,"n_cols":18,"rows_kept":47,"cut":false,"hidden":false,"merged":[]}
["For Quantitative Data, Create Frequency Distribution and Histogram",null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Notes about Skew Right:"]
[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"House Prices"]
[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Data Entry Errors"]
[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Manufacturing Defects"]
[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"Salaries"]
["House Prices ($)  often Skewed Right - a few small values will tend to pull the Average (Mean) down",null,"COUNT function = No. in Sample = n =",{"$f":"COUNT(A7:A47)","v":41},null,null,null,null,null,null,null,null,null,null,null,null,"All these examples start at zero and have no theoretical upper limit, but"]
[{"$f":"IF(MOD(ROW(),7),RANDBETWEEN(95000,500000),RANDBETWEEN(450000,1000000))","v":743605},null,"MAX function",{"$f":"MAX(A7:A47)","v":916654},null,null,null,null,null,null,null,null,null,null,null,null,"the likelihood of getting a high value is low: thus positive/right skew"]
[486638,null,"MIN function",{"$f":"MIN(A7:A47)","v":100849}]
[315193,null,{"$f":"\"Range = (MAX - MIN) = \"&D7&\" - \"&D8&\" =\"","v":"Range = (MAX - MIN) = 916654 - 100849 ="},{"$f":"D7-D8","v":815805}]
[230357,null,"# of Classes = k =",7,"This is a judgment call (the art side of statistics)",null,null,null,null,null,null,null,null,null,null,null,null,"Skew:"]
[121042,null,{"$f":"\"Is 2^\"&D10&\" (\"&2^D10&\") >= \"&D6","v":"Is 2^7 (128) >= 41"},{"$f":"2^D10>=D6","v":true},null,null,null,null,null,null,null,null,null,null,null,null,null,{"$f":"SKEW(A7:A47)","v":1.2685350987031174}]
[499226,null,{"$f":"\"Approximate Class Width = Range/k = \"&D9&\"/\"&D10&\" =\"","v":"Approximate Class Width = Range/k = 815805/7 ="},{"$f":"(D7-D8)/D10","v":116543.57142857143}]
[343588,null,"Class width = ",150000,"This is a judgment call (the art side of statistics)"]
[900930,null,{"$f":"\"Actual Range for Classes = ClassWidth*k = \"&D13&\"*\"&D10&\" =\"","v":"Actual Range for Classes = ClassWidth*k = 150000*7 ="},{"$f":"D13*D10","v":1050000}]
[273808,null,"Lower Limit of First Class (look at MIN then estimate)",0,"This is a judgment call (the art side of statistics)"]
[288003]
[332110,null,null,null,null,"Class","Frequency","% Frequency"]
[391537,null,null,{"$f":"D15","v":0},{"$f":"D18+D$13","v":150000},{"$f":"DOLLAR(D18,0)&\" up to \"&DOLLAR(E18,0)","v":"$0 up to $150,000"},{"$f":"COUNTIF($A$7:$A$47,\"<\"&E18)-COUNTIF($A$7:$A$47,\"<\"&D18)","v":5},{"$f":"G18/G$25","v":0.12195121951219512}]
[114204,null,null,{"$f":"E18","v":150000},300000,"$150,000 up to $300,000",13,0.31707317073170732]
[266647,null,null,300000,450000,"$300,000 up to $450,000",13,0.31707317073170732]
[534171,null,null,450000,600000,"$450,000 up to $600,000",6,0.14634146341463414]
[383153,null,null,600000,750000,"$600,000 up to $750,000",2,4.878048780487805E-2]
[259118,null,null,750000,900000,"$750,000 up to $900,000",0,0]
[424325,null,null,900000,1050000,"$900,000 up to $1,050,000",2,4.878048780487805E-2]
[351026,null,null,null,null,null,{"$f":"SUM(G18:G24)","v":41},{"$f":"SUM(H18:H24)","v":1}]
[321276]
[261231]
[916654]
[309443]
[147339]
[453002]
[409312]
[361713]
[442028]
[484071]
[100849]
[265056]
[212674]
[412454]
[192448]
[182694]
[689910]
[231906]
[182276]
[107531]
[468327]
[198080]

{"sheet":"S(HR)","index":52,"n_rows":47,"n_cols":8,"rows_kept":47,"cut":false,"hidden":false,"merged":[]}
["For Quantitative Data, Create Frequency Distribution and Histogram"]
[]
[]
[]
[]
["Amount Spent at Department Store ($) often Highly Skewed Right - a few small values will tend to pull the Average (Mean) down",null,"COUNT function = No. in Sample = n =",{"$f":"COUNT(A7:A47)","v":41}]
[{"$f":"IF(MOD(ROW(),8),RANDBETWEEN(0,750),RANDBETWEEN(0,8000))","v":300},null,"MAX function",{"$f":"MAX(A7:A47)","v":6644}]
[987,null,"MIN function",{"$f":"MIN(A7:A47)","v":14}]
[400,null,{"$f":"\"Range = (MAX - MIN) = \"&D7&\" - \"&D8&\" =\"","v":"Range = (MAX - MIN) = 6644 - 14 ="},{"$f":"D7-D8","v":6630}]
[158,null,"# of Classes = k =",7,"This is a judgment call (the art side of statistics)"]
[210,null,{"$f":"\"Is 2^\"&D10&\" (\"&2^D10&\") >= \"&D6","v":"Is 2^7 (128) >= 41"},{"$f":"2^D10>=D6","v":true}]
[559,null,{"$f":"\"Approximate Class Width = Range/k = \"&D9&\"/\"&D10&\" =\"","v":"Approximate Class Width = Range/k = 6630/7 ="},{"$f":"(D7-D8)/D10","v":947.14285714285711}]
[325,null,"Class width = ",250,"This is a judgment call (the art side of statistics)"]
[325,null,{"$f":"\"Actual Range for Classes = ClassWidth*k = \"&D13&\"*\"&D10&\" =\"","v":"Actual Range for Classes = ClassWidth*k = 250*7 ="},{"$f":"D13*D10","v":1750}]
[94,null,"Lower Limit of First Class (look at MIN then estimate)",0,"This is a judgment call (the art side of statistics)"]
[3937,null,"Last 2 range",3500]
[385,null,null,null,null,"Class","Frequency","% Frequency"]
[62,null,null,{"$f":"D15","v":0},{"$f":"D18+D$13","v":250},{"$f":"DOLLAR(D18,0)&\" up to \"&DOLLAR(E18,0)","v":"$0 up to $250"},{"$f":"COUNTIF($A$7:$A$47,\"<\"&E18)-COUNTIF($A$7:$A$47,\"<\"&D18)","v":16},{"$f":"G18/G$25","v":0.3902439024390244}]
[482,null,null,{"$f":"E18","v":250},{"$f":"D19+D$13","v":500},"$250 up to $500",10,0.24390243902439024]
[62,null,null,500,{"$f":"D20+D$13","v":750},"$500 up to $750",10,0.24390243902439024]
[63,null,null,750,{"$f":"D21+D$13","v":1000},"$750 up to $1,000",2,4.878048780487805E-2]
[25,null,null,1000,{"$f":"D22+D$13","v":1250},"$1,000 up to $1,250",0,0]
[213,null,null,1250,{"$f":"D23+D$16","v":4750},"$1,250 up to $4,750",2,4.878048780487805E-2]
[754,null,null,4750,{"$f":"D24+D$16","v":8250},"$4,750 up to $8,250",1,2.4390243902439025E-2]
[295,null,null,null,null,null,{"$f":"SUM(G18:G24)","v":41},{"$f":"SUM(H18:H24)","v":1}]
[537]
[511]
[579]
[53]
[599]
[29]
[2992]
[475]
[662]
[190]
[667]
[14]
[59]
[259]
[6644]
[159]
[539]
[650]
[165]
[505]
[243]
[374]

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[null,null,null,null,null,null,null,null,"Xbar"," =","Sample Mean","\"Xbar\"",null,null,null,null,71,0.5,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,91,0.5]
[null,null,null,null,null,null,null,null,"xi"," =","Particular Value","\"x sub i\"",null,null,null,null,80,0.5,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,92,0.5]
[null,null,null,null,null,null,null,null,"s"," =","Sample Standard Deviation","\"s\"",null,null,null,null,76,0.5,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,92,0.5]
[null,null,null,null,null,null,null,null,"z"," =","Number of Standard Deviations Away From Xbar","\"z\"",null,null,null,null,72,0.5,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,87,0.5]
[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,74,0.5,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,89,0.5]
["Who did better? You or your Brother?",null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,76,0.5,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,81,0.5]
["Your Calculus Test score, Xi =",{"$f":"AA2","v":83},null,{"$f":"IF(B25=\"\",\"\",FIXED(B25,2)&\" standard deviations \"&IF(B25>0,\"above\",\"below\")&\" the mean\")","v":""},null,null,null,null,null,null,null,null,null,null,null,null,87,0.5,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,92,0.5]
["Mean for Calculus Test, Xbar =",{"$f":"T2","v":76},null,{"$f":"IF(B25=\"\",\"\",\"Relative location of a particular x in the data set (as compared to the mean),\")","v":""},null,null,null,null,null,null,null,null,null,null,null,null,72,0.5,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,93,0.5]
["Standard Deviation for Calculus Test, s =",{"$f":"W2","v":7},null,{"$f":"IF(B25=\"\",\"\",\"    in units of standard deviation.\")","v":""},null,null,null,null,null,null,null,null,null,null,null,null,85,0.5,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,97,0.5]
["z = (x - Xbar)/s =",null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,77,0.5,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,96,0.5]
["STANDARDIZE function",null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,63,0.5,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,87,0.5]
["Brother's History Test score, Xi =",{"$f":"AR2","v":86},null,{"$f":"IF(B30=\"\",\"\",FIXED(B30,2)&\" standard deviations \"&IF(B30>0,\"above\",\"below\")&\" the mean\")","v":""},null,null,null,null,null,null,null,null,null,null,null,null,92,0.5,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,87,0.5]
["Mean for History Test, Xbar =",{"$f":"AK2","v":91},null,{"$f":"IF(B30=\"\",\"\",\"Relative location of a particular x in the data set (as compared to the mean),\")","v":""}]
["Standard Deviation for History Test, s =",{"$f":"AN2","v":4},null,{"$f":"IF(B30=\"\",\"\",\"    in units of standard deviation.\")","v":""}]
["z = (x - Xbar)/s ="]
["STANDARDIZE function"]
["Joe's Calculus Test score, Xi =",{"$f":"T2","v":76}]
["z score"]

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[null,null,null,null,null,null,null,null,"Xbar"," =","Sample Mean","\"Xbar\"",null,null,null,null,71,0.5,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,91,0.5]
[null,null,null,null,null,null,null,null,"xi"," =","Particular Value","\"x sub i\"",null,null,null,null,80,0.5,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,92,0.5]
[null,null,null,null,null,null,null,null,"s"," =","Sample Standard Deviation","\"s\"",null,null,null,null,76,0.5,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,92,0.5]
[null,null,null,null,null,null,null,null,"z"," =","Number of Standard Deviations Away From Xbar","\"z\"",null,null,null,null,72,0.5,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,87,0.5]
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["Who did better? You or your Brother?",null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,76,0.5,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,81,0.5]
["Your Calculus Test score, Xi =",{"$f":"AA2","v":83},null,{"$f":"IF(B25=\"\",\"\",FIXED(B25,2)&\" standard deviations \"&IF(B25>0,\"above\",\"below\")&\" the mean\")","v":"1.00 standard deviations above the mean"},null,null,null,null,null,null,null,null,null,null,null,null,87,0.5,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,92,0.5]
["Mean for Calculus Test, Xbar =",{"$f":"T2","v":76},null,{"$f":"IF(B25=\"\",\"\",\"Relative location of a particular x in the data set (as compared to the mean),\")","v":"Relative location of a particular x in the data set (as compared to the mean),"},null,null,null,null,null,null,null,null,null,null,null,null,72,0.5,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,93,0.5]
["Standard Deviation for Calculus Test, s =",{"$f":"W2","v":7},null,{"$f":"IF(B25=\"\",\"\",\"    in units of standard deviation.\")","v":"    in units of standard deviation."},null,null,null,null,null,null,null,null,null,null,null,null,85,0.5,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,97,0.5]
["z = (x - Xbar)/s =",{"$f":"(B21-B22)/B23","v":1},null,null,null,null,null,null,null,null,null,null,null,null,null,null,77,0.5,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,96,0.5]
["STANDARDIZE function",{"$f":"STANDARDIZE(B21,B22,B23)","v":1},null,null,null,null,null,null,null,null,null,null,null,null,null,null,63,0.5,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,87,0.5]
["Brother's History Test score, Xi =",{"$f":"AR2","v":86},null,{"$f":"IF(B30=\"\",\"\",FIXED(B30,2)&\" standard deviations \"&IF(B30>0,\"above\",\"below\")&\" the mean\")","v":"-1.25 standard deviations below the mean"},null,null,null,null,null,null,null,null,null,null,null,null,92,0.5,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,87,0.5]
["Mean for History Test, Xbar =",{"$f":"AK2","v":91},null,{"$f":"IF(B30=\"\",\"\",\"Relative location of a particular x in the data set (as compared to the mean),\")","v":"Relative location of a particular x in the data set (as compared to the mean),"}]
["Standard Deviation for History Test, s =",{"$f":"AN2","v":4},null,{"$f":"IF(B30=\"\",\"\",\"    in units of standard deviation.\")","v":"    in units of standard deviation."}]
["z = (x - Xbar)/s =",{"$f":"(B26-B27)/B28","v":-1.25}]
["STANDARDIZE function",{"$f":"STANDARDIZE(B26,B27,B28)","v":-1.25}]
["Joe's Calculus Test score, Xi =",{"$f":"T2","v":76}]
["z score",{"$f":"(B31-B22)/B23","v":0}]

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[]
["Test 1 Scores","z",null,"Lower","Upper","Category","Frequency",null,null,null,{"$f":"1-1/2^2","v":0.75}]
[68,null,null,0,{"$f":"D6+10","v":10},{"$f":"D6&CHAR(10)&\"up to\"&CHAR(10)&E6","v":"0\nup to\n10"},{"$f":"COUNTIFS(A$6:A$105,\">=\"&D6,A$6:A$105,\"<\"&E6)","v":0}]
[58,null,null,{"$f":"E6","v":10},{"$f":"D7+10","v":20},{"$f":"D7&CHAR(10)&\"up to\"&CHAR(10)&E7","v":"10\nup to\n20"},0]
[74,null,null,{"$f":"E7","v":20},{"$f":"D8+10","v":30},"20\nup to\n30",0]
[53,null,null,30,40,"30\nup to\n40",8]
[70,null,null,40,50,"40\nup to\n50",6]
[62,null,null,50,60,"50\nup to\n60",33]
[55,null,null,60,70,"60\nup to\n70",33]
[60,null,null,70,80,"70\nup to\n80",7]
[39,null,null,{"$f":"E13","v":80},{"$f":"D14+10","v":90},"80\nup to\n90",10]
[61,null,null,90,100,"90\nup to\n100",3]
[62,null,null,{"$f":"E15","v":100},{"$f":"D16+10","v":110},"100\nup to\n110",0]
[34]
[57]
[65]
[70]
[31]
[58]
[40]
[62]
[52]
[61]
[53]
[60]
[50]
[56]
[67]
[57]
[59]
[81]
[84]
[55]
[32]
[63]
[56]
[57]
[63]
[66]
[76]
[32]
[40]
[67]
[57]
[58]
[61]
[63]
[67]
[58]
[57]
[65]
[46]
[62]
[59]
[45]
[84]
[68]
[80]
[89]
[55]
[91]
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[63]
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[37]
[62]
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[]
["Test 1 Scores","z",null,"Lower","Upper","Category","Frequency",null,null,null,{"$f":"1-1/2^2","v":0.75}]
[68,{"$f":"(A6-$B$2)/$B$3","v":0.61538461538461542},null,0,{"$f":"D6+10","v":10},{"$f":"D6&CHAR(10)&\"up to\"&CHAR(10)&E6","v":"0\nup to\n10"},{"$f":"COUNTIFS(A$6:A$105,\">=\"&D6,A$6:A$105,\"<\"&E6)","v":0}]
[58,{"$f":"(A7-$B$2)/$B$3","v":-0.15384615384615385},null,{"$f":"E6","v":10},{"$f":"D7+10","v":20},{"$f":"D7&CHAR(10)&\"up to\"&CHAR(10)&E7","v":"10\nup to\n20"},0]
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[53,-0.53846153846153844,null,30,40,"30\nup to\n40",8]
[70,0.76923076923076927,null,40,50,"40\nup to\n50",6]
[62,0.15384615384615385,null,50,60,"50\nup to\n60",33]
[55,-0.38461538461538464,null,60,70,"60\nup to\n70",33]
[60,0,null,70,80,"70\nup to\n80",7]
[39,-1.6153846153846154,null,{"$f":"E13","v":80},{"$f":"D14+10","v":90},"80\nup to\n90",10]
[61,7.6923076923076927E-2,null,90,100,"90\nup to\n100",3]
[62,0.15384615384615385,null,{"$f":"E15","v":100},{"$f":"D16+10","v":110},"100\nup to\n110",0]
[34,-2]
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[65,0.38461538461538464]
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[31,-2.2307692307692308]
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[61,7.6923076923076927E-2]
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[67,0.53846153846153844]
[57,-0.23076923076923078]
[59,-7.6923076923076927E-2]
[81,1.6153846153846154]
[84,1.8461538461538463]
[55,-0.38461538461538464]
[32,-2.1538461538461537]
[63,0.23076923076923078]
[56,-0.30769230769230771]
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[76,1.2307692307692308]
[32,-2.1538461538461537]
[40,-1.5384615384615385]
[67,0.53846153846153844]
[57,-0.23076923076923078]
[58,-0.15384615384615385]
[61,7.6923076923076927E-2]
[63,0.23076923076923078]
[67,0.53846153846153844]
[58,-0.15384615384615385]
[57,-0.23076923076923078]
[65,0.38461538461538464]
[46,-1.0769230769230769]
[62,0.15384615384615385]
[59,-7.6923076923076927E-2]
[45,-1.1538461538461537]
[84,1.8461538461538463]
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[80,1.5384615384615385]
[89,2.2307692307692308]
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[74,1.0769230769230769]
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[60,0]
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[84,1.8461538461538463]
[58,-0.15384615384615385]
[92,{"$f":"(A71-$B$2)/$B$3","v":2.4615384615384617}]
[85,1.9230769230769231]
[57,-0.23076923076923078]
[82,1.6923076923076923]
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[30,-2.3076923076923075]
[58,-0.15384615384615385]
[84,1.8461538461538463]
[59,-7.6923076923076927E-2]
[58,-0.15384615384615385]
[41,-1.4615384615384615]
[72,0.92307692307692313]
[79,1.4615384615384615]
[67,0.53846153846153844]
[63,0.23076923076923078]
[56,-0.30769230769230771]
[63,0.23076923076923078]
[58,-0.15384615384615385]
[59,-7.6923076923076927E-2]
[37,-1.7692307692307692]
[62,0.15384615384615385]
[55,-0.38461538461538464]
[41,-1.4615384615384615]
[92,2.4615384615384617]
[60,0]
[63,0.23076923076923078]
[57,-0.23076923076923078]
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[]
["Heights of Students in Class (inches)","z",null,null,null,null,null,null,null,null,null,null,null,null,"Category","Frequency"]
[73,null,null,null,null,null,null,null,null,null,null,null,null,null,61,{"$f":"COUNTIFS(A$5:A$90,O5)","v":1}]
[68,null,null,null,null,null,null,null,null,null,null,null,null,null,62,0]
[77,null,null,null,null,null,null,null,null,null,null,null,null,null,63,1]
[75,null,null,null,null,null,null,null,null,null,null,null,null,null,64,2]
[61,null,null,null,null,null,null,null,null,null,null,null,null,null,65,3]
[78,null,null,null,null,null,null,null,null,null,null,null,null,null,66,3]
[69,null,null,null,null,null,null,null,null,null,null,null,null,null,67,5]
[70,null,null,null,null,null,null,null,null,null,null,null,null,null,68,5]
[75,null,null,null,null,null,null,null,null,null,null,null,null,null,69,6]
[73,null,null,null,null,null,null,null,null,null,null,null,null,null,70,8]
[71,null,null,null,null,null,null,null,null,null,null,null,null,null,71,10]
[70,null,null,null,null,null,null,null,null,null,null,null,null,null,72,9]
[72,null,null,null,null,null,null,null,null,null,null,null,null,null,73,8]
[69,null,null,null,null,null,null,null,null,null,null,null,null,null,74,6]
[79,null,null,null,null,null,null,null,null,null,null,null,null,null,75,6]
[71,null,null,null,null,null,null,null,null,null,null,null,null,null,76,4]
[67,null,null,null,null,null,null,null,null,null,null,null,null,null,77,4]
[73,null,null,null,null,null,null,null,null,null,null,null,null,null,78,1]
[63,null,null,null,null,null,null,null,null,null,null,null,null,null,79,2]
[74,null,null,null,null,null,null,null,null,null,null,null,null,null,80,0]
[76,null,null,null,null,null,null,null,null,null,null,null,null,null,81,1]
[65,null,null,null,null,null,null,null,null,null,null,null,null,null,82,0]
[75,null,null,null,null,null,null,null,null,null,null,null,null,null,83,1]
[72]
[76]
[71]
[70]
[64]
[74]
[83]
[65]
[74]
[67]
[72]
[71]
[68]
[74]
[73]
[70]
[74]
[76]
[69]
[71]
[72]
[77]
[66]
[77]
[70]
[64]
[66]
[70]
[71]
[70]
[72]
[68]
[71]
[70]
[75]
[67]
[69]
[75]
[67]
[73]
[72]
[79]
[71]
[74]
[68]
[72]
[72]
[67]
[71]
[76]
[73]
[69]
[71]
[73]
[68]
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[65]
[69]
[72]
[66]
[75]
[81]
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{"sheet":"z(3an)","index":58,"n_rows":90,"n_cols":16,"rows_kept":90,"cut":false,"hidden":false,"merged":[]}
["Xbar (inches)",{"$f":"ROUND(AVERAGE(A5:A90),0)","v":71},null,null,null,null,null,null,null,null,null,null,null,null,"Min",{"$f":"MIN(A5:A90)","v":61}]
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[]
["Heights of Students in Class (inches)","z",null,null,null,null,null,null,null,null,null,null,null,null,"Category","Frequency"]
[73,{"$f":"(A5-$B$1)/$B$2","v":0.5},null,null,null,null,null,null,null,null,null,null,null,null,61,{"$f":"COUNTIFS(A$5:A$90,O5)","v":1}]
[68,{"$f":"(A6-$B$1)/$B$2","v":-0.75},null,null,null,null,null,null,null,null,null,null,null,null,62,0]
[77,1.5,null,null,null,null,null,null,null,null,null,null,null,null,63,1]
[75,1,null,null,null,null,null,null,null,null,null,null,null,null,64,2]
[61,-2.5,null,null,null,null,null,null,null,null,null,null,null,null,65,3]
[78,1.75,null,null,null,null,null,null,null,null,null,null,null,null,66,3]
[69,-0.5,null,null,null,null,null,null,null,null,null,null,null,null,67,5]
[70,-0.25,null,null,null,null,null,null,null,null,null,null,null,null,68,5]
[75,1,null,null,null,null,null,null,null,null,null,null,null,null,69,6]
[73,0.5,null,null,null,null,null,null,null,null,null,null,null,null,70,8]
[71,0,null,null,null,null,null,null,null,null,null,null,null,null,71,10]
[70,-0.25,null,null,null,null,null,null,null,null,null,null,null,null,72,9]
[72,0.25,null,null,null,null,null,null,null,null,null,null,null,null,73,8]
[69,-0.5,null,null,null,null,null,null,null,null,null,null,null,null,74,6]
[79,2,null,null,null,null,null,null,null,null,null,null,null,null,75,6]
[71,0,null,null,null,null,null,null,null,null,null,null,null,null,76,4]
[67,-1,null,null,null,null,null,null,null,null,null,null,null,null,77,4]
[73,0.5,null,null,null,null,null,null,null,null,null,null,null,null,78,1]
[63,-2,null,null,null,null,null,null,null,null,null,null,null,null,79,2]
[74,0.75,null,null,null,null,null,null,null,null,null,null,null,null,80,0]
[76,1.25,null,null,null,null,null,null,null,null,null,null,null,null,81,1]
[65,-1.5,null,null,null,null,null,null,null,null,null,null,null,null,82,0]
[75,1,null,null,null,null,null,null,null,null,null,null,null,null,83,1]
[72,0.25]
[76,1.25]
[71,0]
[70,-0.25]
[64,-1.75]
[74,0.75]
[83,3]
[65,-1.5]
[74,0.75]
[67,-1]
[72,0.25]
[71,0]
[68,-0.75]
[74,0.75]
[73,0.5]
[70,-0.25]
[74,0.75]
[76,1.25]
[69,-0.5]
[71,0]
[72,0.25]
[77,1.5]
[66,-1.25]
[77,1.5]
[70,-0.25]
[64,-1.75]
[66,-1.25]
[70,-0.25]
[71,0]
[70,-0.25]
[72,0.25]
[68,-0.75]
[71,0]
[70,-0.25]
[75,1]
[67,-1]
[69,-0.5]
[75,1]
[67,-1]
[73,0.5]
[72,0.25]
[79,2]
[71,{"$f":"(A70-$B$1)/$B$2","v":0}]
[74,0.75]
[68,-0.75]
[72,0.25]
[72,0.25]
[67,-1]
[71,0]
[76,1.25]
[73,0.5]
[69,-0.5]
[71,0]
[73,0.5]
[68,-0.75]
[73,0.5]
[65,-1.5]
[69,-0.5]
[72,0.25]
[66,-1.25]
[75,1]
[81,2.5]
[77,1.5]

{"sheet":"Chebyshev","index":59,"n_rows":73,"n_cols":3,"rows_kept":73,"cut":false,"hidden":false,"merged":[]}
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
["Test Mean",{"$f":"'z(2)'!$B$2","v":60}]
["Test SD",{"$f":"'z(2)'!$B$3","v":13}]
[]
["We want to know what proportion of test scores must lie between +/- 2 SD of the Mean?"]
["z",2]
["Proportion",null,{"$f":"IF(ISNUMBER(B31),\"At least \"&TEXT(B31,\"0.0%\")&\", of the data values must be within +/- \"&B30&\" SD of the Mean\",\"\")","v":""}]
["Given the Proportions from Chebyshev's Theorem, what are the low and high values?"]
["Low"]
["High",null,{"$f":"IF(ISNUMBER(B34),\"At least \"&TEXT(B31,\"0.0%\")&\", of the data values must be within \"&B33&\" and \"&B34,\"\")","v":""}]
["We want to know what proportion of test scores must lie between +/- 2 SD of the Mean?"]
["z",3]
["Proportion",null,{"$f":"IF(ISNUMBER(B37),\"At least \"&TEXT(B37,\"0.0%\")&\", of the data values must be within +/- \"&B36&\" SD of the Mean\",\"\")","v":""}]
["Given the Proportions from Chebyshev's Theorem, what are the low and high values?"]
["Low"]
["High",null,{"$f":"IF(ISNUMBER(B40),\"At least \"&TEXT(B37,\"0.0%\")&\", of the data values must be within \"&B39&\" and \"&B40,\"\")","v":""}]
[]
[]
[]
[{"$f":"\"According to Chebyshev's Theorem, at least what percent of any data set will be within \"&B45&\" standard deviation of the mean?\"","v":"According to Chebyshev's Theorem, at least what percent of any data set will be within 1.8 standard deviation of the mean?"}]
["z =",1.8]
["Percent:",null,{"$f":"IF(B46=\"\",\"\",\"Accoring to Chebyshev's Theorem, at least \"&TEXT(B46,\"0.00%\")&\" of any data set will be within +/- 1.8 standard deviation of the mean.\")","v":""}]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
["Chebyshev's Theorem: Proportion of data values that lie between +/- a given number of standard deviations"]
["Rule: At least (1 - 1/z^2) of the values in any data set will be within z standard deviations of the mean, where z is any value greater than 1.\n"]
["Real power of this Theorem: Applies to any data set regardless of the shape of the distribution of the data!"]
["Chebyshev's Theorem:"]
["At least 0.75, or 75.00%, of the data values must be within z = 2 standard deviations of the mean"]
["At least 0.89, or 89.00%, of the data values must be within z = 3 standard deviations of the mean"]
["At least 0.94, or 94.00%, of the data values must be within z = 4 standard deviations of the mean"]

{"sheet":"Chebyshev (an)","index":60,"n_rows":73,"n_cols":3,"rows_kept":73,"cut":false,"hidden":false,"merged":[]}
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
["Test Mean",{"$f":"'z(2)'!$B$2","v":60}]
["Test SD",{"$f":"'z(2)'!$B$3","v":13}]
[]
["We want to know what proportion of test scores must lie between +/- 2 SD of the Mean?"]
["z",2]
["Proportion",{"$f":"1-1/B30^2","v":0.75},{"$f":"IF(ISNUMBER(B31),\"At least \"&TEXT(B31,\"0.0%\")&\", of the data values must be within +/- \"&B30&\" SD of the Mean\",\"\")","v":"At least 75.0%, of the data values must be within +/- 2 SD of the Mean"}]
["Given the Proportions from Chebyshev's Theorem, what are the low and high values?"]
["Low",{"$f":"B26-B30*B27","v":34}]
["High",{"$f":"B26+B30*B27","v":86},{"$f":"IF(ISNUMBER(B34),\"At least \"&TEXT(B31,\"0.0%\")&\", of the data values must be within \"&B33&\" and \"&B34,\"\")","v":"At least 75.0%, of the data values must be within 34 and 86"}]
["We want to know what proportion of test scores must lie between +/- 2 SD of the Mean?"]
["z",3]
["Proportion",{"$f":"1-1/B36^2","v":0.88888888888888884},{"$f":"IF(ISNUMBER(B37),\"At least \"&TEXT(B37,\"0.0%\")&\", of the data values must be within +/- \"&B36&\" SD of the Mean\",\"\")","v":"At least 88.9%, of the data values must be within +/- 3 SD of the Mean"}]
["Given the Proportions from Chebyshev's Theorem, what are the low and high values?"]
["Low",{"$f":"B26-B36*B27","v":21}]
["High",{"$f":"B26+B36*B27","v":99},{"$f":"IF(ISNUMBER(B40),\"At least \"&TEXT(B37,\"0.0%\")&\", of the data values must be within \"&B39&\" and \"&B40,\"\")","v":"At least 88.9%, of the data values must be within 21 and 99"}]
[]
[]
[]
[{"$f":"\"According to Chebyshev's Theorem, at least what percent of any data set will be within \"&B45&\" standard deviation of the mean?\"","v":"According to Chebyshev's Theorem, at least what percent of any data set will be within 1.8 standard deviation of the mean?"}]
["z =",1.8]
["Percent:",{"$f":"1-1/B45^2","v":0.69135802469135799},{"$f":"IF(B46=\"\",\"\",\"Accoring to Chebyshev's Theorem, at least \"&TEXT(B46,\"0.00%\")&\" of any data set will be within +/- 1.8 standard deviation of the mean.\")","v":"Accoring to Chebyshev's Theorem, at least 69.14% of any data set will be within +/- 1.8 standard deviation of the mean."}]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
["Chebyshev's Theorem: Proportion of data values that lie between +/- a given number of standard deviations"]
["Rule: At least (1 - 1/z^2) of the values in any data set will be within z standard deviations of the mean, where z is any value greater than 1.\n"]
["Real power of this Theorem: Applies to any data set regardless of the shape of the distribution of the data!"]
["Chebyshev's Theorem:"]
["At least 0.75, or 75.00%, of the data values must be within z = 2 standard deviations of the mean"]
["At least 0.89, or 89.00%, of the data values must be within z = 3 standard deviations of the mean"]
["At least 0.94, or 94.00%, of the data values must be within z = 4 standard deviations of the mean"]

{"sheet":"Empirical Rule","index":61,"n_rows":113,"n_cols":13,"rows_kept":113,"cut":false,"hidden":false,"merged":[]}
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
["Empirical Rule:"]
["About 99.0% of the values will lie between +/- 3 SD"]
["About 95.0% of the values will lie between +/- 2 SD"]
["About 68.0% of the values will lie between +/- 1 SD"]
[]
["Mean Height",{"$f":"'z(3)'!B1","v":71},null,"*assuming population distribution is bell shaped."]
["SD",{"$f":"'z(3)'!B2","v":4}]
["68% of the values lie between what two values?",null,null,null,null,null,null,null,null,0.68,"heights"]
["z",1]
["Lower"]
["Upper",null,{"$f":"IF(ISNUMBER(B39),\"About \"&TEXT(J36,\"0%\")&\" of the \"&K36&\" will be between \"&TEXT(B38,\"0\\\"\"\")&\" and \"&TEXT(B39,\"0\\\"\"\"),\"\")","v":""}]
["95% of the values lie between what two values?",null,null,null,null,null,null,null,null,0.95,"heights"]
["z",2]
["Lower"]
["Upper",null,{"$f":"IF(ISNUMBER(B43),\"About \"&TEXT(J40,\"0%\")&\" of the \"&K40&\" will be between \"&TEXT(B42,\"0\\\"\"\")&\" and \"&TEXT(B43,\"0\\\"\"\"),\"\")","v":""}]
["99% of the values lie between what two values?",null,null,null,null,null,null,null,null,0.99,"heights"]
["z",3]
["Lower"]
["Upper",null,{"$f":"IF(ISNUMBER(B47),\"About \"&TEXT(J44,\"0%\")&\" of the \"&K44&\" will be between \"&TEXT(B46,\"0\\\"\"\")&\" and \"&TEXT(B47,\"0\\\"\"\"),\"\")","v":""}]
[]
["Empirical Rule:"]
["About 99.0% of the values will lie between +/- 3 SD"]
["About 95.0% of the values will lie between +/- 2 SD"]
["About 68.0% of the values will lie between +/- 1 SD"]
[]
["Mean Gas Price",2.31,null,"*assuming population distribution is bell shaped.",null,null,null,null,"Chapter 6 and later we will be using this rule to check reasonableness of peoples claims."]
["SD",0.12]
["68% of the values lie between what two values?",null,null,null,null,null,null,null,null,0.68,"prices"]
["z",1]
["Lower"]
["Upper",null,{"$f":"IF(ISNUMBER(B59),\"About \"&TEXT(J56,\"0%\")&\" of the \"&K56&\" will be between \"&TEXT(B58,\"$0.00\")&\" and \"&TEXT(B59,\"$0.00\"),\"\")","v":""}]
["95% of the values lie between what two values?",null,null,null,null,null,null,null,null,0.95,"prices"]
["z",2]
["Lower"]
["Upper",null,{"$f":"IF(ISNUMBER(B63),\"About \"&TEXT(J60,\"0%\")&\" of the \"&K60&\" will be between \"&TEXT(B62,\"$0.00\")&\" and \"&TEXT(B63,\"$0.00\"),\"\")","v":""}]
["99% of the values lie between what two values?",null,null,null,null,null,null,null,null,0.99,"prices"]
["z",3]
["Lower"]
["Upper",null,{"$f":"IF(ISNUMBER(B67),\"About \"&TEXT(J64,\"0%\")&\" of the \"&K64&\" will be between \"&TEXT(B66,\"$0.00\")&\" and \"&TEXT(B67,\"$0.00\"),\"\")","v":""}]
[]
["Empirical Rule:"]
["About 99.0% of the values will lie between +/- 3 SD"]
["About 95.0% of the values will lie between +/- 2 SD"]
["About 68.0% of the values will lie between +/- 1 SD"]
[]
["Mean Cereal Box Weight",32,"oz.","*assuming population distribution is bell shaped.",null,null,null,null,"Chapter 6 and later we will be using this rule to check whether production machines are filling correctly"]
["SD",0.35,"oz."]
["68% of the values lie between what two values?",null,null,null,null,null,null,null,null,0.68,"weight"]
["z",1]
["Lower"]
["Upper",null,{"$f":"IF(ISNUMBER(B79),\"About \"&TEXT(J76,\"0%\")&\" of the \"&K76&\" will be between \"&TEXT(B78,\"0.00 oz.\")&\" and \"&TEXT(B79,\"0.00 oz.\"),\"\")","v":""}]
["95% of the values lie between what two values?",null,null,null,null,null,null,null,null,0.95,"weight"]
["z",2]
["Lower"]
["Upper",null,{"$f":"IF(ISNUMBER(B83),\"About \"&TEXT(J80,\"0%\")&\" of the \"&K80&\" will be between \"&TEXT(B82,\"0.00 oz.\")&\" and \"&TEXT(B83,\"0.00 oz.\"),\"\")","v":""}]
["95% of the values lie between what two values?",null,null,null,null,null,null,null,null,0.99,"weight"]
["z",3]
["Lower"]
["Upper",null,{"$f":"IF(ISNUMBER(B87),\"About \"&TEXT(J84,\"0%\")&\" of the \"&K84&\" will be between \"&TEXT(B86,\"0.00 oz.\")&\" and \"&TEXT(B87,\"0.00 oz.\"),\"\")","v":""}]
[]
[]
["Empirical Rule Areas:"]
["SD","%","1/2 area"]
[" +/- 1 SD",0.68]
[" +/- 2 SD",0.95]
[" +/- 3 SD",0.99]
[]
["Mean Gas Price",2.31]
["SD",0.12]
[{"$f":"\"What is the probability of getting a gas price between \"&DOLLAR(B99)&\" and \"&DOLLAR(B100)&\"?\"","v":"What is the probability of getting a gas price between $2.31 and $2.55?"}]
["Lower",2.31]
["Upper",{"$f":"B96+B97*2","v":2.5499999999999998}]
[{"$f":"\"z \"&A99","v":"z Lower"}]
[{"$f":"\"z \"&A100","v":"z Upper"},null,null,null,null,null,null,null,null,null,null,null,{"$f":"_xlfn.NORM.S.DIST(B102,1)-_xlfn.NORM.S.DIST(B101,1)","v":0}]
["probability",null,{"$f":"IF(ISNUMBER(B103),\"The probability of getting a gas price between \"&DOLLAR(B99)&\" and \"&DOLLAR(B100)&\" is approximately \"&TEXT(B103,\"0.0%\"),\"\")","v":""}]
[{"$f":"\"What is the probability of getting a gas price between \"&DOLLAR(B105)&\" and \"&DOLLAR(B106)&\"?\"","v":"What is the probability of getting a gas price between $2.07 and $2.43?"}]
["Lower",{"$f":"B96-B97*2","v":2.0700000000000003}]
["Upper",{"$f":"B96+B97*1","v":2.4300000000000002}]
[{"$f":"\"z \"&A105","v":"z Lower"}]
[{"$f":"\"z \"&A106","v":"z Upper"},null,null,null,null,null,null,null,null,null,null,null,{"$f":"_xlfn.NORM.S.DIST(B108,1)-_xlfn.NORM.S.DIST(B107,1)","v":0}]
["probability",null,{"$f":"IF(ISNUMBER(B109),\"The probability of getting a gas price between \"&DOLLAR(B105)&\" and \"&DOLLAR(B106)&\" is approximately \"&TEXT(B109,\"0.0%\"),\"\")","v":""}]
[{"$f":"\"What is the probability of getting a gas price above \"&DOLLAR(B111)","v":"What is the probability of getting a gas price above $2.19"}]
["Lower",{"$f":"B96-B97","v":2.19}]
[{"$f":"\"z \"&A111","v":"z Lower"},null,null,null,null,null,null,null,null,null,null,null,{"$f":"1-_xlfn.NORM.S.DIST(B112,1)","v":0.5}]
["probability",null,{"$f":"IF(ISNUMBER(B113),\"The probability of getting a gas price above \"&DOLLAR(B111)&\" is approximately \"&TEXT(B113,\"0.0%\"),\"\")","v":""}]

{"sheet":"HeightChart","index":62,"n_rows":1230,"n_cols":29,"rows_kept":1230,"cut":false,"hidden":true,"merged":[]}
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[null,null,null,null,"s",{"$f":"'z(3)'!B2","v":4},null,null,null,-3,{"$f":"$F$1+J2*$F$2","v":59},null,"z","z = # of SD from Mean",null,null,null,null,null,null,-3,90,0,null,null,"Bell Shaped Distribution"]
[null,null,null,null,null,null,null,null,null,-2,{"$f":"$F$1+J3*$F$2","v":63},null,"Label",{"$f":"N1&CHAR(10)&N2","v":"X = Hieght\nz = # of SD from Mean"},null,null,null,null,null,null,-3,90,0.15,null,null,"Gaussian Distribution"]
[null,null,null,null,"SD",3,2,1,null,-1,67,null,null,null,null,null,null,null,null,null,"z","X",{"$f":"\"Line\"&U5","v":"Line-2"},null,null,"Symmetrical Distribution"]
[null,null,null,null,"Label",{"$f":"TEXT(TRUNC(F6,2),\"0.0%\")","v":"99.0%"},{"$f":"TEXT(TRUNC(G6,2),\"0.0%\")","v":"95.0%"},"68.0%",null,0,71,null,null,null,null,null,null,null,null,null,-2,{"$f":"$F$1+U5*$F$2","v":63},0]
[null,null,null,null,"Area",{"$f":"_xlfn.NORM.S.DIST(F4,1)-_xlfn.NORM.S.DIST(-F4,1)","v":0.99730020393673979},{"$f":"_xlfn.NORM.S.DIST(G4,1)-_xlfn.NORM.S.DIST(-G4,1)","v":0.95449973610364158},{"$f":"_xlfn.NORM.S.DIST(H4,1)-_xlfn.NORM.S.DIST(-H4,1)","v":0.68268949213708607},null,1,75,null,null,null,null,null,null,null,null,null,-2,{"$f":"$F$1+U6*$F$2","v":63},0.12]
[null,null,null,null,"Lower",{"$f":"$F$1-F$4*$F$2","v":59},{"$f":"$F$1-G$4*$F$2","v":63},{"$f":"$F$1-H$4*$F$2","v":67},null,2,79,null,null,null,null,null,null,null,null,null,"z","X",{"$f":"\"Line\"&U8","v":"Line-1"}]
[null,null,null,null,"Upper",{"$f":"$F$1+F$4*$F$2","v":83},{"$f":"$F$1+G$4*$F$2","v":79},{"$f":"$F$1+H$4*$F$2","v":75},null,3,83,null,null,null,null,null,null,null,null,null,-1,{"$f":"$F$1+U8*$F$2","v":67},0]
[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,-1,{"$f":"$F$1+U9*$F$2","v":67},0.11]
["z","x","z Chart","x Chart","All Area Under Curve = 100% = All Possibilities. Each half = 50%.",{"$f":"\"About \"&TEXT(TRUNC(F6,2),\"0.0%\")&\" of the values will lie between +/- \"&F4&\" SD, or between \"&ROUND(F7,0)&\" and \"&ROUND(F8,0)&\" inches\"","v":"About 99.0% of the values will lie between +/- 3 SD, or between 59 and 83 inches"},{"$f":"\"About \"&TEXT(TRUNC(G6,2),\"0.0%\")&\" of the values will lie between +/- \"&G4&\" SD, or between \"&ROUND(G7,0)&\" and \"&ROUND(G8,0)&\" inches\"","v":"About 95.0% of the values will lie between +/- 2 SD, or between 63 and 79 inches"},{"$f":"\"About \"&TEXT(TRUNC(H6,2),\"0.0%\")&\" of the values will lie between +/- \"&H4&\" SD, or between \"&ROUND(H7,0)&\" and \"&ROUND(H8,0)&\" inches\"","v":"About 68.0% of the values will lie between +/- 1 SD, or between 67 and 75 inches"},{"$f":"\"Mean = \"&F1&\", where z = 0\"","v":"Mean = 71, where z = 0"},null,null,null,null,null,null,null,null,null,null,null,"z","X",{"$f":"\"Line\"&U11","v":"Line0"}]
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[-7.75,{"$f":"INT((ROWS(B$11:B14)-1)/20)+40+0.05*MOD(ROWS(B$11:B14)-1,20)","v":40.15},"","",1.2088025996670304E-14,"","","","",null,null,null,null,null,null,null,null,null,null,null,0,71]
[-7.75,{"$f":"INT((ROWS(B$11:B15)-1)/20)+40+0.05*MOD(ROWS(B$11:B15)-1,20)","v":40.200000000000003},"","",1.3310370930632453E-14,"","","","",null,null,null,null,null,null,null,null,null,null,null,0,71]
[-7.75,{"$f":"INT((ROWS(B$11:B16)-1)/20)+40+0.05*MOD(ROWS(B$11:B16)-1,20)","v":40.25},"","",1.4654029891968206E-14,"","","","",null,null,null,null,null,null,null,null,null,null,null,0,71]
[-7.75,{"$f":"INT((ROWS(B$11:B17)-1)/20)+40+0.05*MOD(ROWS(B$11:B17)-1,20)","v":40.299999999999997},"","",1.6130808269532778E-14,"","","","",null,null,null,null,null,null,null,null,null,null,null,0,71]
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[-7.75,{"$f":"INT((ROWS(B$11:B19)-1)/20)+40+0.05*MOD(ROWS(B$11:B19)-1,20)","v":40.4},"","",1.9536675629424992E-14,"","","","",null,null,null,null,null,null,null,null,null,null,null,0,71]
[-7.75,{"$f":"INT((ROWS(B$11:B20)-1)/20)+40+0.05*MOD(ROWS(B$11:B20)-1,20)","v":40.450000000000003},"","",2.1495430455396374E-14,"","","","",null,null,null,null,null,null,null,null,null,null,null,0,71]
[-7.75,{"$f":"INT((ROWS(B$11:B21)-1)/20)+40+0.05*MOD(ROWS(B$11:B21)-1,20)","v":40.5},"","",2.3646875701966308E-14,"","","","",null,null,null,null,null,null,null,null,null,null,null,0,71]
[-7.75,{"$f":"INT((ROWS(B$11:B22)-1)/20)+40+0.05*MOD(ROWS(B$11:B22)-1,20)","v":40.549999999999997},"","",2.6009591545432855E-14,"","","","",null,null,null,null,null,null,null,null,null,null,null,0,71]
[-7.75,{"$f":"INT((ROWS(B$11:B23)-1)/20)+40+0.05*MOD(ROWS(B$11:B23)-1,20)","v":40.6},"","",2.8603912254503424E-14,"","","","",null,null,null,null,null,null,null,null,null,null,null,0,71]
[-7.75,{"$f":"INT((ROWS(B$11:B24)-1)/20)+40+0.05*MOD(ROWS(B$11:B24)-1,20)","v":40.65},"","",3.1452088111544286E-14,"","","","",null,null,null,null,null,null,null,null,null,null,null,0,71]
[-7.75,{"$f":"INT((ROWS(B$11:B25)-1)/20)+40+0.05*MOD(ROWS(B$11:B25)-1,20)","v":40.700000000000003},"","",3.4578461927252152E-14,"","","","",null,null,null,null,null,null,null,null,null,null,null,0,71]
[-7.75,{"$f":"INT((ROWS(B$11:B26)-1)/20)+40+0.05*MOD(ROWS(B$11:B26)-1,20)","v":40.75},"","",3.8009661430662397E-14,"","","","",null,null,null,null,null,null,null,null,null,null,null,0,71]
[-7.75,{"$f":"INT((ROWS(B$11:B27)-1)/20)+40+0.05*MOD(ROWS(B$11:B27)-1,20)","v":40.799999999999997},"","",4.1774808925959093E-14,"","","","",null,null,null,null,null,null,null,null,null,null,null,0,71]
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[-7.5,{"$f":"INT((ROWS(B$11:B36)-1)/20)+40+0.05*MOD(ROWS(B$11:B36)-1,20)","v":41.25},"","",9.706107073388052E-14,"","","","",null,null,null,null,null,null,null,null,null,null,null,0,71]
[-7.5,{"$f":"INT((ROWS(B$11:B37)-1)/20)+40+0.05*MOD(ROWS(B$11:B37)-1,20)","v":41.3},"","",1.0650916103799159E-13,"","","","",null,null,null,null,null,null,null,null,null,null,null,0,71]
[-7.5,{"$f":"INT((ROWS(B$11:B38)-1)/20)+40+0.05*MOD(ROWS(B$11:B38)-1,20)","v":41.35},"","",1.1685868398382626E-13,"","","","",null,null,null,null,null,null,null,null,null,null,null,0,71]
[-7.5,{"$f":"INT((ROWS(B$11:B39)-1)/20)+40+0.05*MOD(ROWS(B$11:B39)-1,20)","v":41.4},"","",1.2819384091991657E-13,"","","","",null,null,null,null,null,null,null,null,null,null,null,0,71]
[-7.5,{"$f":"INT((ROWS(B$11:B40)-1)/20)+40+0.05*MOD(ROWS(B$11:B40)-1,20)","v":41.45},"","",1.4060652345839503E-13,"","","","",null,null,null,null,null,null,null,null,null,null,null,0,71]
[-7.5,{"$f":"INT((ROWS(B$11:B41)-1)/20)+40+0.05*MOD(ROWS(B$11:B41)-1,20)","v":41.5},"","",1.5419699921345145E-13,"","","","",null,null,null,null,null,null,null,null,null,null,null,0,71]
[-7.5,{"$f":"INT((ROWS(B$11:B42)-1)/20)+40+0.05*MOD(ROWS(B$11:B42)-1,20)","v":41.55},"","",1.6907465713740136E-13,"","","","",null,null,null,null,null,null,null,null,null,null,null,0,71]
[-7.5,{"$f":"INT((ROWS(B$11:B43)-1)/20)+40+0.05*MOD(ROWS(B$11:B43)-1,20)","v":41.6},"","",1.8535881749260904E-13,"","","","",null,null,null,null,null,null,null,null,null,null,null,0,{"$f":"$F$1+U43*$F$2","v":71}]
[-7.5,{"$f":"INT((ROWS(B$11:B44)-1)/20)+40+0.05*MOD(ROWS(B$11:B44)-1,20)","v":41.65},"","",2.0317961191086871E-13,"","","","",null,null,null,null,null,null,null,null,null,null,null,0,71]
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[-7.5,{"$f":"INT((ROWS(B$11:B47)-1)/20)+40+0.05*MOD(ROWS(B$11:B47)-1,20)","v":41.8},"","",2.6734594678853913E-13,"","","","",null,null,null,null,null,null,null,null,null,null,null,0,71]
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[7,{"$f":"INT((ROWS(B$11:B1197)-1)/20)+40+0.05*MOD(ROWS(B$11:B1197)-1,20)","v":99.3},"",{"$f":"IF(ISNUMBER(MATCH(B1197,$K$2:$K$8,0)),TEXT(B1197,\"0\\\"\"\")&CHAR(10)&TEXT((B1197-$F$1)/$F$2,\" 0 z\"),\"\")","v":""},1.3471290813492094E-12,"","","",""]
[7,{"$f":"INT((ROWS(B$11:B1198)-1)/20)+40+0.05*MOD(ROWS(B$11:B1198)-1,20)","v":99.35},"","",1.2330121727561535E-12,"","","",""]
[7,{"$f":"INT((ROWS(B$11:B1199)-1)/20)+40+0.05*MOD(ROWS(B$11:B1199)-1,20)","v":99.4},"","",1.1283859193013672E-12,"","","",""]
[7,{"$f":"INT((ROWS(B$11:B1200)-1)/20)+40+0.05*MOD(ROWS(B$11:B1200)-1,20)","v":99.45},"","",1.0324763051722252E-12,"","","",""]
[7,{"$f":"INT((ROWS(B$11:B1201)-1)/20)+40+0.05*MOD(ROWS(B$11:B1201)-1,20)","v":99.5},"","",9.4457113641602255E-13,"","","",""]
[7,{"$f":"INT((ROWS(B$11:B1202)-1)/20)+40+0.05*MOD(ROWS(B$11:B1202)-1,20)","v":99.55},"","",8.6401521239362959E-13,"","","",""]
[7,{"$f":"INT((ROWS(B$11:B1203)-1)/20)+40+0.05*MOD(ROWS(B$11:B1203)-1,20)","v":99.6},"","",7.9020586536727443E-13,"","","",""]
[7,{"$f":"INT((ROWS(B$11:B1204)-1)/20)+40+0.05*MOD(ROWS(B$11:B1204)-1,20)","v":99.65},"","",7.2258884088228648E-13,"","","",""]
[7,{"$f":"INT((ROWS(B$11:B1205)-1)/20)+40+0.05*MOD(ROWS(B$11:B1205)-1,20)","v":99.7},"","",6.6065449357549174E-13,"","","",""]
[7,{"$f":"INT((ROWS(B$11:B1206)-1)/20)+40+0.05*MOD(ROWS(B$11:B1206)-1,20)","v":99.75},"","",6.0393427487763541E-13,"","","",""]
[7,{"$f":"INT((ROWS(B$11:B1207)-1)/20)+40+0.05*MOD(ROWS(B$11:B1207)-1,20)","v":99.8},"","",5.5199749078428875E-13,"","","",""]
[7,{"$f":"INT((ROWS(B$11:B1208)-1)/20)+40+0.05*MOD(ROWS(B$11:B1208)-1,20)","v":99.85},"","",5.0444830945556132E-13,"","","",""]
[7,{"$f":"INT((ROWS(B$11:B1209)-1)/20)+40+0.05*MOD(ROWS(B$11:B1209)-1,20)","v":99.9},"","",4.6092299988006369E-13,"","","",""]
[7,{"$f":"INT((ROWS(B$11:B1210)-1)/20)+40+0.05*MOD(ROWS(B$11:B1210)-1,20)","v":99.95},"","",4.2108738420910114E-13,"","","",""]
[7.25,{"$f":"INT((ROWS(B$11:B1211)-1)/20)+40+0.05*MOD(ROWS(B$11:B1211)-1,20)","v":100},"","",3.8463448764031875E-13,{"$f":"IF(AND($B1211>=F$7,$B1211<=F$8),E1211,\"\")","v":""},"","",""]
[7.25,{"$f":"INT((ROWS(B$11:B1212)-1)/20)+40+0.05*MOD(ROWS(B$11:B1212)-1,20)","v":100.05},"","",3.5128237091357995E-13,"","","",""]
[7.25,{"$f":"INT((ROWS(B$11:B1213)-1)/20)+40+0.05*MOD(ROWS(B$11:B1213)-1,20)","v":100.1},"","",3.2077213158023951E-13,"","","",""]
[7.25,{"$f":"INT((ROWS(B$11:B1214)-1)/20)+40+0.05*MOD(ROWS(B$11:B1214)-1,20)","v":100.15},"","",2.9286606122770584E-13,"","","",""]
[7.25,{"$f":"INT((ROWS(B$11:B1215)-1)/20)+40+0.05*MOD(ROWS(B$11:B1215)-1,20)","v":100.2},"","",2.6734594678853913E-13,"","","",""]
[7.25,{"$f":"INT((ROWS(B$11:B1216)-1)/20)+40+0.05*MOD(ROWS(B$11:B1216)-1,20)","v":100.25},"","",2.4401150494270107E-13,"","","",""]
[7.25,{"$f":"INT((ROWS(B$11:B1217)-1)/20)+40+0.05*MOD(ROWS(B$11:B1217)-1,20)","v":100.3},"","",2.2267893943799217E-13,"","","",{"$f":"IF(AND(B1217>=$F$1-0.1,B1217<=$F$1+0.1),E1217,\"\")","v":""}]
[7.25,{"$f":"INT((ROWS(B$11:B1218)-1)/20)+40+0.05*MOD(ROWS(B$11:B1218)-1,20)","v":100.35},"","",2.0317961191087161E-13,"","","",""]
[7.25,{"$f":"INT((ROWS(B$11:B1219)-1)/20)+40+0.05*MOD(ROWS(B$11:B1219)-1,20)","v":100.4},"","",1.8535881749260641E-13,"","","",""]
[7.25,{"$f":"INT((ROWS(B$11:B1220)-1)/20)+40+0.05*MOD(ROWS(B$11:B1220)-1,20)","v":100.45},"","",1.6907465713740136E-13,"","","",""]
[7.25,{"$f":"INT((ROWS(B$11:B1221)-1)/20)+40+0.05*MOD(ROWS(B$11:B1221)-1,20)","v":100.5},"","",1.5419699921345145E-13,"","","",""]
[7.25,{"$f":"INT((ROWS(B$11:B1222)-1)/20)+40+0.05*MOD(ROWS(B$11:B1222)-1,20)","v":100.55},"","",1.4060652345839503E-13,"","","",""]
[7.25,{"$f":"INT((ROWS(B$11:B1223)-1)/20)+40+0.05*MOD(ROWS(B$11:B1223)-1,20)","v":100.6},"","",1.2819384091991839E-13,"","","",""]
[7.25,{"$f":"INT((ROWS(B$11:B1224)-1)/20)+40+0.05*MOD(ROWS(B$11:B1224)-1,20)","v":100.65},"","",1.16858683983825E-13,"","","",""]
[7.25,{"$f":"INT((ROWS(B$11:B1225)-1)/20)+40+0.05*MOD(ROWS(B$11:B1225)-1,20)","v":100.7},"","",1.0650916103799159E-13,"","","",""]
[7.25,{"$f":"INT((ROWS(B$11:B1226)-1)/20)+40+0.05*MOD(ROWS(B$11:B1226)-1,20)","v":100.75},"","",9.706107073388052E-14,"","","",""]
[7.25,{"$f":"INT((ROWS(B$11:B1227)-1)/20)+40+0.05*MOD(ROWS(B$11:B1227)-1,20)","v":100.8},"","",8.8437271190247646E-14,"","","",""]
[{"$f":"(INT(B1228)-$F$1)/$F$2","v":7.25},{"$f":"INT((ROWS(B$11:B1228)-1)/20)+40+0.05*MOD(ROWS(B$11:B1228)-1,20)","v":100.85},{"$f":"IF(AND(A1228<>A1227,INT(A1228)=A1228),A1228,\"\")","v":""},"",8.0567099838124186E-14,"","","",""]
[7.25,{"$f":"INT((ROWS(B$11:B1229)-1)/20)+40+0.05*MOD(ROWS(B$11:B1229)-1,20)","v":100.9},"","",7.3385839934508384E-14,"","","",""]
[7.25,{"$f":"INT((ROWS(B$11:B1230)-1)/20)+40+0.05*MOD(ROWS(B$11:B1230)-1,20)","v":100.95},"","",6.6834230076107444E-14,"","","",""]

{"sheet":"Empirical Rule (an)","index":63,"n_rows":113,"n_cols":13,"rows_kept":113,"cut":false,"hidden":false,"merged":[]}
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
["Empirical Rule:"]
["About 99.0% of the values will lie between +/- 3 SD"]
["About 95.0% of the values will lie between +/- 2 SD"]
["About 68.0% of the values will lie between +/- 1 SD"]
[]
["Mean Height",{"$f":"'z(3)'!B1","v":71},null,"*assuming population distribution is bell shaped."]
["SD",{"$f":"'z(3)'!B2","v":4}]
["68% of the values lie between what two values?",null,null,null,null,null,null,null,null,0.68,"heights"]
["z",1]
["Lower",{"$f":"B34-B37*B35","v":67}]
["Upper",{"$f":"B34+B37*B35","v":75},{"$f":"IF(ISNUMBER(B39),\"About \"&TEXT(J36,\"0%\")&\" of the \"&K36&\" will be between \"&TEXT(B38,\"0\\\"\"\")&\" and \"&TEXT(B39,\"0\\\"\"\"),\"\")","v":"About 68% of the heights will be between 67\" and 75\""}]
["95% of the values lie between what two values?",null,null,null,null,null,null,null,null,0.95,"heights"]
["z",2]
["Lower",{"$f":"B34-B41*B35","v":63}]
["Upper",{"$f":"B34+B41*B35","v":79},{"$f":"IF(ISNUMBER(B43),\"About \"&TEXT(J40,\"0%\")&\" of the \"&K40&\" will be between \"&TEXT(B42,\"0\\\"\"\")&\" and \"&TEXT(B43,\"0\\\"\"\"),\"\")","v":"About 95% of the heights will be between 63\" and 79\""}]
["99% of the values lie between what two values?",null,null,null,null,null,null,null,null,0.99,"heights"]
["z",3]
["Lower",{"$f":"B34-B45*B35","v":59}]
["Upper",{"$f":"B34+B45*B35","v":83},{"$f":"IF(ISNUMBER(B47),\"About \"&TEXT(J44,\"0%\")&\" of the \"&K44&\" will be between \"&TEXT(B46,\"0\\\"\"\")&\" and \"&TEXT(B47,\"0\\\"\"\"),\"\")","v":"About 99% of the heights will be between 59\" and 83\""}]
[]
["Empirical Rule:"]
["About 99.0% of the values will lie between +/- 3 SD"]
["About 95.0% of the values will lie between +/- 2 SD"]
["About 68.0% of the values will lie between +/- 1 SD"]
[]
["Mean Gas Price",2.31,null,"*assuming population distribution is bell shaped.",null,null,null,null,"Chapter 6 and later we will be using this rule to check reasonableness of peoples claims."]
["SD",0.12]
["68% of the values lie between what two values?",null,null,null,null,null,null,null,null,0.68,"prices"]
["z",1]
["Lower",{"$f":"$B$54-B57*$B$55","v":2.19}]
["Upper",{"$f":"$B$54+B57*$B$55","v":2.4300000000000002},{"$f":"IF(ISNUMBER(B59),\"About \"&TEXT(J56,\"0%\")&\" of the \"&K56&\" will be between \"&TEXT(B58,\"$0.00\")&\" and \"&TEXT(B59,\"$0.00\"),\"\")","v":"About 68% of the prices will be between $2.19 and $2.43"}]
["95% of the values lie between what two values?",null,null,null,null,null,null,null,null,0.95,"prices"]
["z",2]
["Lower",{"$f":"$B$54-B61*$B$55","v":2.0700000000000003}]
["Upper",{"$f":"$B$54+B61*$B$55","v":2.5499999999999998},{"$f":"IF(ISNUMBER(B63),\"About \"&TEXT(J60,\"0%\")&\" of the \"&K60&\" will be between \"&TEXT(B62,\"$0.00\")&\" and \"&TEXT(B63,\"$0.00\"),\"\")","v":"About 95% of the prices will be between $2.07 and $2.55"}]
["99% of the values lie between what two values?",null,null,null,null,null,null,null,null,0.99,"prices"]
["z",3]
["Lower",{"$f":"$B$54-B65*$B$55","v":1.9500000000000002}]
["Upper",{"$f":"$B$54+B65*$B$55","v":2.67},{"$f":"IF(ISNUMBER(B67),\"About \"&TEXT(J64,\"0%\")&\" of the \"&K64&\" will be between \"&TEXT(B66,\"$0.00\")&\" and \"&TEXT(B67,\"$0.00\"),\"\")","v":"About 99% of the prices will be between $1.95 and $2.67"}]
[]
["Empirical Rule:"]
["About 99.0% of the values will lie between +/- 3 SD"]
["About 95.0% of the values will lie between +/- 2 SD"]
["About 68.0% of the values will lie between +/- 1 SD"]
[]
["Mean Cereal Box Weight",32,"oz.","*assuming population distribution is bell shaped.",null,null,null,null,"Chapter 6 and later we will be using this rule to check whether production machines are filling correctly"]
["SD",0.35,"oz."]
["68% of the values lie between what two values?",null,null,null,null,null,null,null,null,0.68,"weight"]
["z",1]
["Lower",{"$f":"$B$74-B77*$B$75","v":31.65}]
["Upper",{"$f":"$B$74+B77*$B$75","v":32.35},{"$f":"IF(ISNUMBER(B79),\"About \"&TEXT(J76,\"0%\")&\" of the \"&K76&\" will be between \"&TEXT(B78,\"0.00 oz.\")&\" and \"&TEXT(B79,\"0.00 oz.\"),\"\")","v":"About 68% of the weight will be between 31.65 oz. and 32.35 oz."}]
["95% of the values lie between what two values?",null,null,null,null,null,null,null,null,0.95,"weight"]
["z",2]
["Lower",{"$f":"$B$74-B81*$B$75","v":31.3}]
["Upper",{"$f":"$B$74+B81*$B$75","v":32.700000000000003},{"$f":"IF(ISNUMBER(B83),\"About \"&TEXT(J80,\"0%\")&\" of the \"&K80&\" will be between \"&TEXT(B82,\"0.00 oz.\")&\" and \"&TEXT(B83,\"0.00 oz.\"),\"\")","v":"About 95% of the weight will be between 31.30 oz. and 32.70 oz."}]
["95% of the values lie between what two values?",null,null,null,null,null,null,null,null,0.99,"weight"]
["z",3]
["Lower",{"$f":"$B$74-B85*$B$75","v":30.95}]
["Upper",{"$f":"$B$74+B85*$B$75","v":33.049999999999997},{"$f":"IF(ISNUMBER(B87),\"About \"&TEXT(J84,\"0%\")&\" of the \"&K84&\" will be between \"&TEXT(B86,\"0.00 oz.\")&\" and \"&TEXT(B87,\"0.00 oz.\"),\"\")","v":"About 99% of the weight will be between 30.95 oz. and 33.05 oz."}]
[]
[]
["Empirical Rule Areas:"]
["SD","%","1/2 area"]
[" +/- 1 SD",0.68,{"$f":"B92/2","v":0.34}]
[" +/- 2 SD",0.95,{"$f":"B93/2","v":0.47499999999999998}]
[" +/- 3 SD",0.99,0.495]
[]
["Mean Gas Price",2.31]
["SD",0.12]
[{"$f":"\"What is the probability of getting a gas price between \"&DOLLAR(B99)&\" and \"&DOLLAR(B100)&\"?\"","v":"What is the probability of getting a gas price between $2.31 and $2.55?"}]
["Lower",2.31]
["Upper",{"$f":"B96+B97*2","v":2.5499999999999998}]
[{"$f":"\"z \"&A99","v":"z Lower"},{"$f":"(B99-$B$96)/$B$97","v":0}]
[{"$f":"\"z \"&A100","v":"z Upper"},{"$f":"(B100-$B$96)/$B$97","v":1.9999999999999982},null,null,null,null,null,null,null,null,null,null,{"$f":"_xlfn.NORM.S.DIST(B102,1)-_xlfn.NORM.S.DIST(B101,1)","v":0.47724986805182068}]
["probability",{"$f":"C93","v":0.47499999999999998},{"$f":"IF(ISNUMBER(B103),\"The probability of getting a gas price between \"&DOLLAR(B99)&\" and \"&DOLLAR(B100)&\" is approximately \"&TEXT(B103,\"0.0%\"),\"\")","v":"The probability of getting a gas price between $2.31 and $2.55 is approximately 47.5%"}]
[{"$f":"\"What is the probability of getting a gas price between \"&DOLLAR(B105)&\" and \"&DOLLAR(B106)&\"?\"","v":"What is the probability of getting a gas price between $2.07 and $2.43?"}]
["Lower",{"$f":"B96-B97*2","v":2.0700000000000003}]
["Upper",{"$f":"B96+B97*1","v":2.4300000000000002}]
[{"$f":"\"z \"&A105","v":"z Lower"},{"$f":"(B105-$B$96)/$B$97","v":-1.9999999999999982}]
[{"$f":"\"z \"&A106","v":"z Upper"},{"$f":"(B106-$B$96)/$B$97","v":1.0000000000000009},null,null,null,null,null,null,null,null,null,null,{"$f":"_xlfn.NORM.S.DIST(B108,1)-_xlfn.NORM.S.DIST(B107,1)","v":0.81859461412036394}]
["probability",{"$f":"C93+C92","v":0.81499999999999995},{"$f":"IF(ISNUMBER(B109),\"The probability of getting a gas price between \"&DOLLAR(B105)&\" and \"&DOLLAR(B106)&\" is approximately \"&TEXT(B109,\"0.0%\"),\"\")","v":"The probability of getting a gas price between $2.07 and $2.43 is approximately 81.5%"}]
[{"$f":"\"What is the probability of getting a gas price above \"&DOLLAR(B111)","v":"What is the probability of getting a gas price above $2.19"}]
["Lower",{"$f":"B96-B97","v":2.19}]
[{"$f":"\"z \"&A111","v":"z Lower"},{"$f":"(B111-B96)/B97","v":-1.0000000000000009},null,null,null,null,null,null,null,null,null,null,{"$f":"1-_xlfn.NORM.S.DIST(B112,1)","v":0.84134474606854326}]
["probability",{"$f":"0.5+C92","v":0.84000000000000008},{"$f":"IF(ISNUMBER(B113),\"The probability of getting a gas price above \"&DOLLAR(B111)&\" is approximately \"&TEXT(B113,\"0.0%\"),\"\")","v":"The probability of getting a gas price above $2.19 is approximately 84.0%"}]

{"sheet":"Bell&Cheb","index":64,"n_rows":19,"n_cols":7,"rows_kept":19,"cut":false,"hidden":false,"merged":[]}
["If the distribution of weights for a Lettuce Filling Machine are normally distributed (Bell Shape) with a mean of 13 oz and a standard deviation of .1 oz, what percent of the values should lie between 2 standard deviations and what are the low and high values associated with the percentage?"]
[]
["Mean",13,"oz"]
["SD",0.1,"oz"]
["# SD",2]
["Percent =",0.95,null,"Empirical Rule"]
["Lower Values"]
["Upper Value"]
[{"$f":"IF(B8=\"\",\"\",\"Because the distribution is bell shaped, we can say that approximately \"&TEXT(B6,\"0.00%\")&\" of the values should lie between \"&B8&\" oz. and \"&B7&\" oz.\")","v":""}]
[]
["Mean",13,"oz"]
["SD",0.1,"oz"]
["# SD",2]
["Percent =",0.75,null,"Chebyshev's Theorem",null,null,{"$f":"1-1/B13^2","v":0.75}]
["Lower Values"]
["Upper Value"]
[{"$f":"IF(B16=\"\",\"\",\"Because the distribution is not bell shaped, we can say that approximately \"&TEXT(B14,\"0.00%\")&\" of the values should lie between \"&B16&\" oz. and \"&B15&\" oz.\")","v":""}]
[]
["."]

{"sheet":"Bell&Cheb (an)","index":65,"n_rows":19,"n_cols":7,"rows_kept":19,"cut":false,"hidden":false,"merged":[]}
["If the distribution of weights for a Lettuce Filling Machine are normally distributed (Bell Shape) with a mean of 13 oz and a standard deviation of .1 oz, what percent of the values should lie between 2 standard deviations and what are the low and high values associated with the percentage?"]
[]
["Mean",13,"oz"]
["SD",0.1,"oz"]
["# SD",2]
["Percent =",0.95,null,"Empirical Rule"]
["Lower Values",{"$f":"B3-B4*B5","v":12.8}]
["Upper Value",{"$f":"B3+B4*B5","v":13.2}]
[{"$f":"IF(B8=\"\",\"\",\"Because the distribution is bell shaped, we can say that approximately \"&TEXT(B6,\"0.00%\")&\" of the values should lie between \"&B8&\" oz. and \"&B7&\" oz.\")","v":"Because the distribution is bell shaped, we can say that approximately 95.00% of the values should lie between 13.2 oz. and 12.8 oz."}]
[]
["Mean",13,"oz"]
["SD",0.1,"oz"]
["# SD",2]
["Percent =",0.75,null,"Chebyshev's Theorem",null,null,{"$f":"1-1/B13^2","v":0.75}]
["Lower Values",{"$f":"B11-B12*B13","v":12.8}]
["Upper Value",{"$f":"B11+B12*B13","v":13.2}]
[{"$f":"IF(B16=\"\",\"\",\"Because the distribution is not bell shaped, we can say that approximately \"&TEXT(B14,\"0.00%\")&\" of the values should lie between \"&B16&\" oz. and \"&B15&\" oz.\")","v":"Because the distribution is not bell shaped, we can say that approximately 75.00% of the values should lie between 13.2 oz. and 12.8 oz."}]
[]
["."]

{"sheet":"5 N S","index":66,"n_rows":124,"n_cols":28,"rows_kept":124,"cut":false,"hidden":false,"merged":[]}
["5 Number Summary Provides information like:"]
["Minimum Value "]
["Maximum Value"]
["Median"]
["Range, Max - Min"]
["Quartiles"]
["Interquartile Range = Qart 3 - Quart 1 = range for middle 50%"]
["Basis for build Box Plot"]
[]
["For 5 Number Summary Use:"]
[]
["MIN, MAX, QUARTILE.EXC Function",null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,{"$f":"CHOOSE(RANDBETWEEN(1,3),RANDBETWEEN(3000,6000),RANDBETWEEN(3800,4800),INT(_xlfn.NORM.INV(RAND(),4300,500)))","v":5663}]
[1,"Min",null,null,"Quartiles"]
[2,"Quartile 1",null,null,1]
[3,"Quartile 2",null,null,2]
[4,"Quartile 3",null,null,3]
[5,"Max"]
[]
["Sample of Monthly Salary for Accounting in Oakland:"]
[]
["Salaries"]
[3822]
[4024]
[4015]
[4442]
[4174]
[3751]
[5802]
[4068]
[4239]
[4426]
[4246]
[4279]
[4431]
[4623]
[4228]
[4311]
[4524]
[4634]
[5490]
[4441]
[3541]
[4383]
[4155]
[4590]
[3891]
[5942]
[3957]
[3234]
[4224]
[4474]
[5942]
[4533]
[4563]
[3893]
[5367]
[3593]
[4710]
[4899]
[4381]
[3894]
[3877]
[5414]
[4837]
[3718]
[4437]
[4466]
[4312]
[4623]
[4512]
[4186]
[4316]
[4836]
[5620]
[4085]
[4104]
[3366]
[4250]
[4438]
[4614]
[4207]
[3945]
[4323]
[4750]
[3950]
[5180]
[4735]
[3904]
[4663]
[4612]
[4544]
[4724]
[4674]
[3184]
[4351]
[4182]
[3851]
[3373]
[3401]
[5492]
[4048]
[4011]
[4077]
[4003]
[4355]
[5280]
[4033]
[2510]
[3987]
[5767]
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{"sheet":"5 N S (an)","index":67,"n_rows":124,"n_cols":28,"rows_kept":124,"cut":false,"hidden":false,"merged":[]}
["5 Number Summary Provides information like:"]
["Minimum Value "]
["Maximum Value"]
["Median"]
["Range, Max - Min"]
["Quartiles"]
["Interquartile Range = Qart 3 - Quart 1 = range for middle 50%"]
["Basis for build Box Plot"]
[]
["For 5 Number Summary Use:"]
[]
["MIN, MAX, QUARTILE.EXC Function",null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,{"$f":"CHOOSE(RANDBETWEEN(1,3),RANDBETWEEN(3000,6000),RANDBETWEEN(3800,4800),INT(_xlfn.NORM.INV(RAND(),4300,500)))","v":4587}]
[1,"Min",{"$f":"MIN(A22:A124)","v":2510},null,"Quartiles"]
[2,"Quartile 1",{"$f":"_xlfn.QUARTILE.EXC($A$22:$A$124,E14)","v":4003},null,1]
[3,"Quartile 2",{"$f":"_xlfn.QUARTILE.EXC($A$22:$A$124,E15)","v":4319},null,2]
[4,"Quartile 3",{"$f":"_xlfn.QUARTILE.EXC($A$22:$A$124,E16)","v":4664},null,3]
[5,"Max",{"$f":"MAX(A22:A124)","v":6987}]
[]
["Sample of Monthly Salary for Accounting in Oakland:"]
[]
["Salaries"]
[3822]
[4024]
[4015]
[4442]
[4174]
[3751]
[5802]
[4068]
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{"sheet":"5 Number & Box Blot","index":68,"n_rows":53,"n_cols":22,"rows_kept":53,"cut":false,"hidden":false,"merged":[]}
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
["Quiz Scores",null,"Min",null,null,"Quartile",null,null,null,null,null,null,null,"Plot Points:",null,null,null,null,"Picture Link:"]
[15,null,"Quartile 1",null,null,1,null,null,null,null,null,null,null,"Min",{"$f":"D16-D15","v":0},null,null,null,{"$f":"C16","v":"Min"},0,{"$f":"C20","v":"Max"},{"$f":"D20","v":0}]
[20,null,"Median",null,null,2,null,null,null,null,null,null,null,"Quartile 1",{"$f":"D17-D16","v":0},null,null,null,"Quartile 1",0,{"$f":"C24","v":"Lower Limit"},{"$f":"IF($D$24<0,0,$D$24)","v":0}]
[25,null,"Quartile 3",null,null,3,null,null,null,null,null,null,null,"Median",{"$f":"D18-D17","v":0},null,null,null,"Median",0,{"$f":"C25","v":"Upper Limit"},{"$f":"D25","v":0}]
[25,null,"Max",null,null,null,null,null,null,null,null,null,null,"Quartile 3",{"$f":"D19-D18","v":0},null,null,null,"Quartile 3",0]
[27,null,"Interquartile Range",null,null,null,null,null,null,null,null,null,null,"Max",{"$f":"D20-D19","v":0},null,"Y"]
[28,null,"Multiplier",1.5,null,null,null,null,null,null,null,null,null,{"$f":"C30","v":"Mean"},{"$f":"D30","v":0},null,1.5]
[30]
[25,null,"Lower Limit",null,null,{"$f":"\"Quartile 1 - \"&D22&\"*Interquartile Range, or 0.\"","v":"Quartile 1 - 1.5*Interquartile Range, or 0."}]
[10,null,"Upper Limit",null,null,{"$f":"\"Quartile 3 + \"&D22&\"*Interquartile Range\"","v":"Quartile 3 + 1.5*Interquartile Range"}]
[35,null,null,null,null,null,null,null,null,null,null,null,null,null,{"$f":"C24&\" X \"","v":"Lower Limit X "},"Y",null,{"$f":"C25&\" X\"","v":"Upper Limit X"},"Y"]
[27,null,"# Outliers Below",null,null,null,null,null,null,null,null,null,null,null,{"$f":"IF($D$24<0,0,$D$24)","v":0},{"$f":"0","v":0},null,{"$f":"D25","v":0},0]
[29,null,"# Outliers Above",null,null,null,null,null,null,null,null,null,null,null,{"$f":"IF($D$24<0,0,$D$24)","v":0},1.5,null,{"$f":"D25","v":0},1.5]
[31]
[28,null,"Mean"]
[11]
[12]
[34]
[null,null,"Reference video for how to create Box Plots in Excel:"]
[null,null,"Excel 2010 Statistics #28: Box & Whisker Plot: Stacked Bar with Mean Point Plotted and Outlier Lines "]
[null,null,"https://www.youtube.com/watch?v=bgaN446TQXo"]
[]
[null,null,"Outlier Notes:"]
[null,null,"Quartile 1 - 1.5*Interquartile Range, or 0."]
[null,null,"Quartile 3 + 1.5*Interquartile Range"]
[null,null,"or:"]
[null,null,"Quartile 1 - 2.2*Interquartile Range, or 0."]
[null,null,"Quartile 3 + 2.2*Interquartile Range"]
[null,null,"Textbook uses Multiplier of 1.5"]
[null,null,"Other Multipliers can be used, like 2.2"]
[null,null,"Video Title: \"The Right Way to Detect Outliers - Outlier Labeling Rule\""]
[null,null,"https://www.youtube.com/watch?v=WSflSmcNRFI"]
[null,null,"https://www.youtube.com/watch?v=2HmopqF6V6w"]
[null,null,"https://www.youtube.com/watch?v=bRdC1u9veg8"]
[null,null,"The references in video are:"]
[null,null,"Tukey, J.W. (1977). Exploratory Data Analysis. Reading, MA: Addison-Wesley."]
[null,null,"Hoaglin, D.C., Iglewicz, B., and Tukey, J.W. (1986). Performance of some resistant rules for outlier labeling, Journal of American Statistical Association, 81, 991-999."]
[null,null,"Hoaglin, D. C., and Iglewicz, B. (1987), Fine tuning some resistant rules for outlier labeling, Journal of American Statistical Association, 82, 1147-1149."]

{"sheet":"5 Number & Box Blot (an)","index":69,"n_rows":53,"n_cols":22,"rows_kept":53,"cut":false,"hidden":false,"merged":[]}
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
["Quiz Scores",null,"Min",{"$f":"MIN($A$17:$A$33)","v":10},null,"Quartile",null,null,null,null,null,null,null,"Plot Points:",null,null,null,null,"Picture Link:"]
[15,null,"Quartile 1",{"$f":"_xlfn.QUARTILE.EXC($A$17:$A$33,F17)","v":17.5},null,1,null,null,null,null,null,null,null,"Min",{"$f":"D16-D15","v":10},null,null,null,{"$f":"C16","v":"Min"},10,{"$f":"C20","v":"Max"},{"$f":"D20","v":35}]
[20,null,"Median",{"$f":"_xlfn.QUARTILE.EXC($A$17:$A$33,F18)","v":27},null,2,null,null,null,null,null,null,null,"Quartile 1",{"$f":"D17-D16","v":7.5},null,null,null,"Quartile 1",17.5,{"$f":"C24","v":"Lower Limit"},{"$f":"IF($D$24<0,0,$D$24)","v":0}]
[25,null,"Quartile 3",{"$f":"_xlfn.QUARTILE.EXC($A$17:$A$33,F19)","v":29.5},null,3,null,null,null,null,null,null,null,"Median",{"$f":"D18-D17","v":9.5},null,null,null,"Median",27,{"$f":"C25","v":"Upper Limit"},{"$f":"D25","v":47.5}]
[25,null,"Max",{"$f":"MAX($A$17:$A$33)","v":35},null,null,null,null,null,null,null,null,null,"Quartile 3",{"$f":"D19-D18","v":2.5},null,null,null,"Quartile 3",29.5]
[27,null,"Interquartile Range",{"$f":"D19-D17","v":12},null,null,null,null,null,null,null,null,null,"Max",{"$f":"D20-D19","v":5.5},null,"Y"]
[28,null,"Multiplier",1.5,null,null,null,null,null,null,null,null,null,{"$f":"C30","v":"Mean"},{"$f":"D30","v":24.235294117647058},null,1.5]
[30]
[25,null,"Lower Limit",{"$f":"D17-$D$21*$D$22","v":-0.5},null,{"$f":"\"Quartile 1 - \"&D22&\"*Interquartile Range, or 0.\"","v":"Quartile 1 - 1.5*Interquartile Range, or 0."}]
[10,null,"Upper Limit",{"$f":"D19+$D$21*$D$22","v":47.5},null,{"$f":"\"Quartile 3 + \"&D22&\"*Interquartile Range\"","v":"Quartile 3 + 1.5*Interquartile Range"}]
[35,null,null,null,null,null,null,null,null,null,null,null,null,null,{"$f":"C24&\" X \"","v":"Lower Limit X "},"Y",null,{"$f":"C25&\" X\"","v":"Upper Limit X"},"Y"]
[27,null,"# Outliers Below",null,null,null,null,null,null,null,null,null,null,null,{"$f":"IF($D$24<0,0,$D$24)","v":0},{"$f":"0","v":0},null,{"$f":"D25","v":47.5},0]
[29,null,"# Outliers Above",{"$f":"COUNTIFS(A17:A33,\">\"&D25)","v":0},null,null,null,null,null,null,null,null,null,null,{"$f":"IF($D$24<0,0,$D$24)","v":0},1.5,null,{"$f":"D25","v":47.5},1.5]
[31]
[28,null,"Mean",{"$f":"AVERAGE(A17:A33)","v":24.235294117647058}]
[11]
[12]
[34]
[null,null,"Reference video for how to create Box Plots in Excel:"]
[null,null,"Excel 2010 Statistics #28: Box & Whisker Plot: Stacked Bar with Mean Point Plotted and Outlier Lines "]
[null,null,"https://www.youtube.com/watch?v=bgaN446TQXo"]
[]
[null,null,"Outlier Notes:"]
[null,null,"Quartile 1 - 1.5*Interquartile Range, or 0."]
[null,null,"Quartile 3 + 1.5*Interquartile Range"]
[null,null,"or:"]
[null,null,"Quartile 1 - 2.2*Interquartile Range, or 0."]
[null,null,"Quartile 3 + 2.2*Interquartile Range"]
[null,null,"Textbook uses Multiplier of 1.5"]
[null,null,"Other Multipliers can be used, like 2.2"]
[null,null,"Video Title: \"The Right Way to Detect Outliers - Outlier Labeling Rule\""]
[null,null,"https://www.youtube.com/watch?v=WSflSmcNRFI"]
[null,null,"https://www.youtube.com/watch?v=2HmopqF6V6w"]
[null,null,"https://www.youtube.com/watch?v=bRdC1u9veg8"]
[null,null,"The references in video are:"]
[null,null,"Tukey, J.W. (1977). Exploratory Data Analysis. Reading, MA: Addison-Wesley."]
[null,null,"Hoaglin, D.C., Iglewicz, B., and Tukey, J.W. (1986). Performance of some resistant rules for outlier labeling, Journal of American Statistical Association, 81, 991-999."]
[null,null,"Hoaglin, D. C., and Iglewicz, B. (1987), Fine tuning some resistant rules for outlier labeling, Journal of American Statistical Association, 82, 1147-1149."]

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[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
["No","Sales","z","Outlier = z >3",null,"Mean","SD","Count TRUES"]
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["No","Sales","z","Outlier = z >3",null,"Mean","SD","Count TRUES"]
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[5414]
[4837,null,"Dynamic Ranges:"]
[3718,null,"x","y",null,"x","y"]
[4437,null,{"$f":"OFFSET($C$34,,,$D$30)","v":2510},{"$f":"OFFSET($D$34,,,$D$30)","v":1},null,{"$f":"OFFSET($F$34,,,$G$30)","v":6987},{"$f":"OFFSET($G$34,,,$G$30)","v":1}]
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{"sheet":"2 Variable Notes","index":75,"n_rows":23,"n_cols":1,"rows_kept":23,"cut":false,"hidden":true,"merged":[]}
["Measures of Association or Linear Relationship Between 2 Variables"]
["Covariance"]
["Covariance measures the strength of the linear relationship between 2 variables"]
["Sample Covariance = sxy = (∑(xi - Xbar)*(yi - Ybar))/(n-1)"]
["Use COVARIANCE.S function"]
["Population Covariance = sxy = (∑(xi - µx)*(yi - µy))/N"]
["Use COVARIANCE.P function"]
["Understanding how Covariance works:"]
["Plot a Mean Line for X and a Mean Line for Y to divide chart into 4 quadrants"]
["When preponderance of the \"X-Deviations Times Y-Deviations\" are positive (X-Y markers are quadrant 1 and 3), the line will show a positive liner association or relationship: \"As X Increases, Y Increase\""]
["When preponderance of the \"X-Deviations Times Y-Deviations\" are negative (X-Y markers are quadrant 2 and 4), the line will show a negative liner association or relationship: \"As X Increases, Y decrease\""]
["When preponderance of the \"X-Deviations Times Y-Deviations\" are neither positive or negative (X-Y markers in all quadrants), the line will show no liner association or relationship"]
["Covariance measures the strength of the linear relationship between 2 variables, but has a problem with units.\nBecause the value of covariance depends on units, we may get larger or smaller covariances simply because we change the units from inches to feet or feet to inches.\nTo overcome this problem with units, we can divide the covariance by the product of the standard deviation to the x and y values, sx * sy , to get a relative measure of strength of association or relationship between 2 variables or a measure of strength per unit of standard deviation, sx * sy . This measure is called Correlation Coefficient"]
["Correlation Coefficient = Pearson Product Moment Correlation Coefficient"]
["Correlation Coefficient measure the strength and direction of linear association or relationship."]
["Values range from -1, perfect negative linear relationship (all X-Y markers on  regression line) to +1, perfect positive linear relationship  (all X-Y markers on  regression line) "]
["Values close to +1 show strong positive linear association/relationship\nValues close to -1 show strong negative linear association/relationship\nThe closer to zero the weaker the relationship\nThe closer the X-Y markers are to the regression line, the stronger the relationship\nThe more the X-Y markers deviate from the regression line, the weaker the relationship"]
["Note: Because the Correlation Coefficient measure the strength and direction of a LINEAR relationship, not nonlinear relationships. If you get a correlation mesure near zero, it may be true that there is a very weak linear relationship, but that does not say that there is not some other sort of non-linear relationship."]
["Sample Correlation Coefficient = rxy = sxy /(sx * sy)"]
["Use PEARSON or CORREL functions"]
["Population Correlation Coefficient rxy = sxy /(sx * sy) , r is pronounced \"Row\""]
["Use PEARSON or CORREL functions"]
["Sample Correlation Coefficient, rxy , is a point estimator of the Population Correlation Coefficient, rxy"]

{"sheet":"2 Variable Scatter","index":76,"n_rows":471,"n_cols":28,"rows_kept":471,"cut":false,"hidden":false,"merged":[]}
["Is there an Association or Relationship Between 2 Variables?"]
["What we already did:"]
["Visually, Graphically, we used: Scatter Chart to look and see if there was a relationship"]
["Independent, Predictor Variable = x"]
["Dependent, Predicted Variable = y"]
["Plot Markers: Move along x axis a given amount and then along the y axis a certain amount."]
["Direct, Positive Relationship: As x increases, y increases"]
["Indirect, Negative Relationship: As x increases, y decreases"]
["No relationship: no pattern can be seen"]
[]
["Example 1:","Relationship Between Temperature & Ice Cream Sales?"]
[null,"Independent Variable","Dependent Variable"]
[null,"Predictor Variable","Predicted Variable"]
["Sample Point","x","y"]
[]
["No.","Temperature (F)","Sales Ice Cream"]
[1,91,7113]
[2,45,2044]
[3,46,1108]
[4,83,7093]
[5,76,3902]
[6,96,6676]
[7,75,5403]
[8,42,886]
[9,70,4740]
[10,47,2637]
[11,58,3150]
[]
["Example 2:","Relationship Between Temperature & Chicken Soup Sales?"]
[null,"Independent Variable","Dependent Variable"]
[null,"Predictor Variable","Predicted Variable"]
["Sample Point","x","y"]
[]
["No.","Temperature (F)","Sales Chicken Soup"]
[1,86,3300]
[2,40,8200]
[3,41,8900]
[4,78,3100]
[5,71,4020]
[6,91,1950]
[7,70,2500]
[8,37,6500]
[9,65,6210]
[10,42,5250]
[11,53,7200]
[12,83,2750]
[13,63,7150]
[14,36,7900]
[15,43,6210]
[]
[]
["Example 3:","Relationship Between Study Time & Score?"]
[null,"Independent Variable","Dependent Variable"]
[null,"Predictor Variable","Predicted Variable"]
["Sample Point","x","y"]
[]
["No.","Time Studying (hours)","Score on Test"]
[1,3,49]
[2,11,87]
[3,2,50]
[4,13,89]
[5,8,84]
[6,12,79]
[7,13,100]
[8,4,57]
[9,7,64]
[10,14,98]
[11,7,81]
[12,7,68]
[13,14,88]
[14,4,45]
[15,4,52]
[16,5,15]
[17,12,72]
[18,16,97]
[19,12,89]
[20,14,87]
[21,2,48]
[22,12,92]
[23,11,89]
[24,6,52]
[25,11,84]
[26,14,94]
[27,10,79]
[28,6,59]
[29,10,66]
[30,11,97]
[]
[]
["Example 4:","Relationship Between Number of Years Using Excel and Expert Rating"]
[null,"Independent Variable","Dependent Variable"]
[null,"Predictor Variable","Predicted Variable"]
["Sample Point","x","y"]
[]
["No.","Number Of Years Using Excel","Expert Level (Rating 1 - 10))"]
[1,3,5]
[2,8,1]
[3,6,9]
[4,11,5]
[5,20,3]
[6,7,4]
[7,9,10]
[8,3,6]
[9,19,10]
[10,2,1]
[11,16,2]
[12,12,7]
[13,1,6]
[]
[]
[]
["Example 5:","Relationship Between Temperature and Energy Expense, relationship looks nonlinear: as x increases, y decreases for a while and then increases"]
[]
[]
["Sample Point","x","y"]
[]
["Date","Temperature X","Energy Expense Y"]
["2015-01-12",46,236,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"2015-01-01",34,56,null,20,2.5,null,"Date","Temperature X","Energy Expense Y"]
["2015-01-13",52,304,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"2015-02-01",27,59,null,34,2.15,null,"2015-01-12",{"$f":"RANDBETWEEN(VLOOKUP(Z119,$S$118:$U$130,2),VLOOKUP(Z119,$S$118:$U$130,3))","v":37},{"$f":"RANDBETWEEN(100,300)*VLOOKUP(AA119,$W$118:$X$128,2)","v":539.65}]
["2015-01-14",55,163.5,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"2015-03-01",21,55,null,44,2]
["2015-01-15",46,214,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"2015-04-01",38,69,null,55,1.5]
["2015-01-16",47,210,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"2015-05-01",44,82,null,65,1]
["2015-01-17",50,508,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"2015-06-01",54,78,null,75,1]
["2015-01-18",36,294.55,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"2015-07-01",60,92,null,85,1.5]
["2015-01-19",47,250,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"2015-08-01",70,102,null,90,2]
["2015-01-20",40,371.95,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"2015-09-01",65,97,null,95,2.5]
["2015-01-21",46,478,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"2015-10-01",52,89,null,100,2.75]
["2015-01-22",55,258,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"2015-11-01",45,69,null,110,2.8]
["2015-01-23",40,559,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"2015-12-10",28,65]
["2015-01-24",53,536,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,"2015-12-31",30,55]
["2015-01-25",44,576]
["2015-01-26",48,446]
["2015-01-27",46,300]
["2015-01-28",56,250.5]
["2015-01-29",36,412.79999999999995]
["2015-01-30",38,511.7]
["2015-01-31",37,311.75]
["2015-02-01",44,478]
["2015-02-02",30,282.5]
["2015-02-03",48,476]
["2015-02-04",37,565.44999999999993]
["2015-02-05",43,567.6]
["2015-02-06",39,634.25]
["2015-02-07",34,266.59999999999997]
["2015-02-08",33,345]
["2015-02-09",38,393.45]
["2015-02-10",38,567.6]
["2015-02-11",37,266.59999999999997]
["2015-02-12",35,503.09999999999997]
["2015-02-13",53,242]
["2015-02-14",55,216]
["2015-02-15",58,354]
["2015-02-16",41,432.15]
["2015-02-17",43,406.34999999999997]
["2015-02-18",43,627.79999999999995]
["2015-02-19",32,705]
["2015-02-20",53,534]
["2015-02-21",54,282]
["2015-02-22",56,435]
["2015-02-23",52,380]
["2015-02-24",57,372]
["2015-02-25",51,240]
["2015-02-26",40,346.15]
["2015-02-27",32,577.5]
["2015-02-28",34,406.34999999999997]
["2015-03-01",37,468.7]
["2015-03-02",39,483.75]
["2015-03-03",22,462.5]
["2015-03-04",38,408.5]
["2015-03-05",40,311.75]
["2015-03-06",51,420]
["2015-03-07",32,410]
["2015-03-08",45,400]
["2015-03-09",40,460.09999999999997]
["2015-03-10",52,538]
["2015-03-11",25,597.5]
["2015-03-12",42,389.15]
["2015-03-13",45,228]
["2015-03-14",31,627.5]
["2015-03-15",51,408]
["2015-03-16",34,339.7]
["2015-03-17",21,527.5]
["2015-03-18",55,364.5]
["2015-03-19",24,695]
["2015-03-20",42,462.25]
["2015-03-21",38,277.34999999999997]
["2015-03-22",48,334]
["2015-03-23",40,445.04999999999995]
["2015-03-24",45,496]
["2015-03-25",45,514]
["2015-03-26",46,350]
["2015-03-27",33,697.5]
["2015-03-28",37,258]
["2015-03-29",24,257.5]
["2015-03-30",36,313.89999999999998]
["2015-03-31",37,481.59999999999997]
["2015-04-01",47,594]
["2015-04-02",64,442.5]
["2015-04-03",60,397.5]
["2015-04-04",50,272]
["2015-04-05",62,208.5]
["2015-04-06",50,200]
["2015-04-07",62,301.5]
["2015-04-08",68,187]
["2015-04-09",65,150]
["2015-04-10",57,445.5]
["2015-04-11",53,498]
["2015-04-12",43,599.85]
["2015-04-13",39,393.45]
["2015-04-14",61,196.5]
["2015-04-15",42,457.95]
["2015-04-16",42,225.75]
["2015-04-17",52,598]
["2015-04-18",68,268]
["2015-04-19",50,468]
["2015-04-20",46,236]
["2015-04-21",58,415.5]
["2015-04-22",56,418.5]
["2015-04-23",61,279]
["2015-04-24",56,195]
["2015-04-25",61,325.5]
["2015-04-26",67,283]
["2015-04-27",68,281]
["2015-04-28",61,313.5]
["2015-04-29",52,466]
["2015-04-30",63,351]
["2015-05-01",80,226]
["2015-05-02",75,165]
["2015-05-03",51,532]
["2015-05-04",44,410]
["2015-05-05",79,121]
["2015-05-06",61,340.5]
["2015-05-07",49,264]
["2015-05-08",80,212]
["2015-05-09",47,590]
["2015-05-10",77,153]
["2015-05-11",60,202.5]
["2015-05-12",54,564]
["2015-05-13",44,326]
["2015-05-14",50,308]
["2015-05-15",69,220]
["2015-05-16",59,448.5]
["2015-05-17",81,239]
["2015-05-18",82,227]
["2015-05-19",59,222]
["2015-05-20",47,282]
["2015-05-21",71,225]
["2015-05-22",45,332]
["2015-05-23",65,178]
["2015-05-24",78,290]
["2015-05-25",54,246]
["2015-05-26",46,598]
["2015-05-27",81,186]
["2015-05-28",52,584]
["2015-05-29",79,259]
["2015-05-30",77,162]
["2015-05-31",66,162]
["2015-06-01",60,300]
["2015-06-02",66,228]
["2015-06-03",63,336]
["2015-06-04",76,164]
["2015-06-05",64,238.5]
["2015-06-06",54,548]
["2015-06-07",62,187.5]
["2015-06-08",75,208]
["2015-06-09",60,153]
["2015-06-10",61,343.5]
["2015-06-11",74,275]
["2015-06-12",72,138]
["2015-06-13",77,168]
["2015-06-14",56,289.5]
["2015-06-15",73,156]
["2015-06-16",60,345]
["2015-06-17",72,292]
["2015-06-18",66,100]
["2015-06-19",57,355.5]
["2015-06-20",57,171]
["2015-06-21",78,253]
["2015-06-22",56,303]
["2015-06-23",59,319.5]
["2015-06-24",71,115]
["2015-06-25",66,133]
["2015-06-26",58,318]
["2015-06-27",67,137]
["2015-06-28",66,275]
["2015-06-29",76,132]
["2015-06-30",57,244.5]
["2015-07-01",72,135]
["2015-07-02",62,246]
["2015-07-03",79,222]
["2015-07-04",83,297]
["2015-07-05",71,173]
["2015-07-06",78,154]
["2015-07-07",70,160]
["2015-07-08",81,139]
["2015-07-09",74,103]
["2015-07-10",92,508]
["2015-07-11",78,172]
["2015-07-12",87,429]
["2015-07-13",62,406.5]
["2015-07-14",85,340.5]
["2015-07-15",92,268]
["2015-07-16",84,235]
["2015-07-17",90,396]
["2015-07-18",66,254]
["2015-07-19",67,189]
["2015-07-20",79,293]
["2015-07-21",74,115]
["2015-07-22",84,181]
["2015-07-23",73,200]
["2015-07-24",72,234]
["2015-07-25",89,423]
["2015-07-26",75,102]
["2015-07-27",80,272]
["2015-07-28",65,159]
["2015-07-29",71,281]
["2015-07-30",91,210]
["2015-07-31",89,273]
["2015-08-01",98,310]
["2015-08-02",91,578]
["2015-08-03",82,300]
["2015-08-04",93,584]
["2015-08-05",73,159]
["2015-08-06",99,715]
["2015-08-07",85,426]
["2015-08-08",71,251]
["2015-08-09",90,294]
["2015-08-10",71,288]
["2015-08-11",97,740]
["2015-08-12",100,646.25]
["2015-08-13",96,337.5]
["2015-08-14",75,154]
["2015-08-15",80,153]
["2015-08-16",74,207]
["2015-08-17",84,151]
["2015-08-18",94,308]
["2015-08-19",99,285]
["2015-08-20",94,422]
["2015-08-21",95,400]
["2015-08-22",88,426]
["2015-08-23",83,182]
["2015-08-24",89,201]
["2015-08-25",79,294]
["2015-08-26",92,244]
["2015-08-27",100,379.5]
["2015-08-28",80,257]
["2015-08-29",97,590]
["2015-08-30",70,199]
["2015-08-31",90,248]
["2015-09-01",75,106]
["2015-09-02",86,217.5]
["2015-09-03",91,328]
["2015-09-04",80,133]
["2015-09-05",73,118]
["2015-09-06",85,442.5]
["2015-09-07",95,455]
["2015-09-08",75,188]
["2015-09-09",87,255]
["2015-09-10",80,294]
["2015-09-11",88,304.5]
["2015-09-12",75,138]
["2015-09-13",68,253]
["2015-09-14",70,180]
["2015-09-15",76,210]
["2015-09-16",87,436.5]
["2015-09-17",90,456]
["2015-09-18",67,273]
["2015-09-19",70,283]
["2015-09-20",97,345]
["2015-09-21",90,232]
["2015-09-22",86,301.5]
["2015-09-23",83,298]
["2015-09-24",69,283]
["2015-09-25",68,193]
["2015-09-26",95,742.5]
["2015-09-27",93,528]
["2015-09-28",79,204]
["2015-09-29",87,180]
["2015-09-30",79,230]
["2015-10-01",85,393]
["2015-10-02",89,310.5]
["2015-10-03",71,298]
["2015-10-04",58,424.5]
["2015-10-05",66,116]
["2015-10-06",82,288]
["2015-10-07",56,318]
["2015-10-08",81,291]
["2015-10-09",57,418.5]
["2015-10-10",59,159]
["2015-10-11",54,504]
["2015-10-12",88,231]
["2015-10-13",83,290]
["2015-10-14",85,303]
["2015-10-15",85,223.5]
["2015-10-16",81,300]
["2015-10-17",73,276]
["2015-10-18",71,142]
["2015-10-19",87,285]
["2015-10-20",86,396]
["2015-10-21",79,265]
["2015-10-22",61,151.5]
["2015-10-23",60,361.5]
["2015-10-24",78,210]
["2015-10-25",81,288]
["2015-10-26",79,174]
["2015-10-27",68,260]
["2015-10-28",68,197]
["2015-10-29",60,276]
["2015-10-30",73,137]
["2015-10-31",74,132]
["2015-11-01",46,224]
["2015-11-02",66,131]
["2015-11-03",50,210]
["2015-11-04",49,498]
["2015-11-05",68,284]
["2015-11-06",63,150]
["2015-11-07",57,289.5]
["2015-11-08",62,237]
["2015-11-09",66,136]
["2015-11-10",62,424.5]
["2015-11-11",53,574]
["2015-11-12",63,391.5]
["2015-11-13",61,382.5]
["2015-11-14",64,436.5]
["2015-11-15",51,578]
["2015-11-16",56,328.5]
["2015-11-17",54,590]
["2015-11-18",53,446]
["2015-11-19",50,226]
["2015-11-20",68,231]
["2015-11-21",58,315]
["2015-11-22",49,220]
["2015-11-23",67,264]
["2015-11-24",49,358]
["2015-11-25",47,498]
["2015-11-26",49,448]
["2015-11-27",47,578]
["2015-11-28",62,402]
["2015-11-29",47,466]
["2015-11-30",49,330]
["2015-12-01",64,214.5]
["2015-12-02",62,190.5]
["2015-12-03",60,270]
["2015-12-04",66,109]
["2015-12-05",62,384]
["2015-12-06",66,286]
["2015-12-07",49,278]
["2015-12-08",62,264]
["2015-12-09",65,179]
["2015-12-10",62,265.5]
["2015-12-11",61,357]
["2015-12-12",31,737.5]
["2015-12-13",53,272]
["2015-12-14",52,236]
["2015-12-15",55,267]
["2015-12-16",61,450]
["2015-12-17",50,486]
["2015-12-18",55,217.5]
["2015-12-19",60,274.5]
["2015-12-20",43,221.45]
["2015-12-21",63,174]
["2015-12-22",49,206]
["2015-12-23",40,539.65]
["2015-12-24",33,677.5]
["2015-12-25",62,157.5]
["2015-12-26",32,680]
["2015-12-27",33,580]
["2015-12-28",52,538]
["2015-12-29",32,252.5]
["2015-12-30",59,348]
["2015-12-31",32,387.5]

{"sheet":"2 Variable Xbar Lines","index":77,"n_rows":438,"n_cols":9,"rows_kept":438,"cut":false,"hidden":false,"merged":[]}
["Is there an Association or Relationship Between 2 Variables?"]
["Scatter Plot with Trendline & X and Y Mean Lines"]
["Create Scatter Plot with Trendline & X and Y Mean Lines to divide"]
["chart into four quadrants in order to further define the pattern and"]
["relationship between the two variables"]
[]
["Example 1:","Relationship Between Temperature & Ice Cream Sales?"]
["Sample Point","x","y",null,"Xbar (F)","Y",null,"X","Ybar (IC Sales)"]
[null,null,null,null,{"$f":"B10","v":66.272727272727266},0,null,30,{"$f":"C10","v":4068.3636363636365}]
["Mean",{"$f":"AVERAGE(B13:B23)","v":66.272727272727266},{"$f":"AVERAGE(C13:C23)","v":4068.3636363636365},null,{"$f":"B10","v":66.272727272727266},10000,null,100,{"$f":"C10","v":4068.3636363636365}]
[]
["No.","Temperature (F)","Sales Ice Cream"]
[1,91,7113]
[2,45,2044]
[3,46,1108]
[4,83,7093]
[5,76,3902]
[6,96,6676]
[7,75,5403]
[8,42,886]
[9,70,4740]
[10,47,2637]
[11,58,3150]
[]
[]
[]
[]
[]
["Example 2:","Relationship Between Temperature & Chicken Soup Sales?"]
["Sample Point","x","y",null,"Xbar (F)","Y",null,"X","Ybar (CS Sales)"]
[null,null,null,null,{"$f":"B32","v":61.142857142857146},0,null,30,{"$f":"C32","v":5352.1428571428569}]
["Mean",{"$f":"AVERAGE(B35:B48)","v":61.142857142857146},{"$f":"AVERAGE(C35:C48)","v":5352.1428571428569},null,{"$f":"B32","v":61.142857142857146},10000,null,100,{"$f":"C32","v":5352.1428571428569}]
[]
["No.","Temperature (F)","Sales Chicken Soup"]
[1,86,3300]
[2,40,8200]
[3,41,8900]
[4,78,3100]
[5,71,4020]
[6,91,1950]
[7,70,2500]
[8,37,6500]
[9,65,6210]
[10,42,5250]
[11,53,7200]
[12,83,2750]
[13,63,7150]
[14,36,7900]
[15,43,6210]
[]
[]
["Example 4:","Relationship Between # of Years Using Excel & Expert Rating"]
["Sample Point","x","y",null,"Xbar (Years)","Y",null,"X","Ybar (Rating)"]
[null,null,null,null,{"$f":"B55","v":9},0,null,0,{"$f":"C55","v":5.3076923076923075}]
["Mean",{"$f":"AVERAGE(B58:B71)","v":9},{"$f":"AVERAGE(C58:C71)","v":5.3076923076923075},null,{"$f":"B55","v":9},12,null,22,{"$f":"C55","v":5.3076923076923075}]
[]
["No.","# Of Years Using Excel","Excel Expert Rating"]
[1,3,5]
[2,8,1]
[3,6,9]
[4,11,5]
[5,20,3]
[6,7,4]
[7,9,10]
[8,3,6]
[9,19,10]
[10,2,1]
[11,16,2]
[12,12,7]
[13,1,6]
[]
[]
[]
["Example 5:","Relationship Between Temperature and Energy Expense, relationship looks nonlinear: as x increases, y decreases for a while and then increases"]
["Sample Point","x","y",null,"Xbar (Temp)","Y",null,"X","Ybar (Energy)"]
[null,null,null,null,{"$f":"B77","v":62.389830508474574},0,null,0,{"$f":"C77","v":327.58149717514124}]
["Mean",{"$f":"AVERAGE(B85:B438)","v":62.389830508474574},{"$f":"AVERAGE(C85:C438)","v":327.58149717514124},null,{"$f":"B77","v":62.389830508474574},800,null,120,{"$f":"C77","v":327.58149717514124}]
[]
["Example 5:","Relationship Between Temperature and Energy Expense, relationship looks nonlinear: as x increases, y decreases for a while and then increases"]
[]
[]
["Sample Point","x","y"]
[]
["Date","Temperature X","Energy Expense Y"]
["2015-01-12",46,236]
["2015-01-13",52,304]
["2015-01-14",55,163.5]
["2015-01-15",46,214]
["2015-01-16",47,210]
["2015-01-17",50,508]
["2015-01-18",36,294.55]
["2015-01-19",47,250]
["2015-01-20",40,371.95]
["2015-01-21",46,478]
["2015-01-22",55,258]
["2015-01-23",40,559]
["2015-01-24",53,536]
["2015-01-25",44,576]
["2015-01-26",48,446]
["2015-01-27",46,300]
["2015-01-28",56,250.5]
["2015-01-29",36,412.79999999999995]
["2015-01-30",38,511.7]
["2015-01-31",37,311.75]
["2015-02-01",44,478]
["2015-02-02",30,282.5]
["2015-02-03",48,476]
["2015-02-04",37,565.44999999999993]
["2015-02-05",43,567.6]
["2015-02-06",39,634.25]
["2015-02-07",34,266.59999999999997]
["2015-02-08",33,345]
["2015-02-09",38,393.45]
["2015-02-10",38,567.6]
["2015-02-11",37,266.59999999999997]
["2015-02-12",35,503.09999999999997]
["2015-02-13",53,242]
["2015-02-14",55,216]
["2015-02-15",58,354]
["2015-02-16",41,432.15]
["2015-02-17",43,406.34999999999997]
["2015-02-18",43,627.79999999999995]
["2015-02-19",32,705]
["2015-02-20",53,534]
["2015-02-21",54,282]
["2015-02-22",56,435]
["2015-02-23",52,380]
["2015-02-24",57,372]
["2015-02-25",51,240]
["2015-02-26",40,346.15]
["2015-02-27",32,577.5]
["2015-02-28",34,406.34999999999997]
["2015-03-01",37,468.7]
["2015-03-02",39,483.75]
["2015-03-03",22,462.5]
["2015-03-04",38,408.5]
["2015-03-05",40,311.75]
["2015-03-06",51,420]
["2015-03-07",32,410]
["2015-03-08",45,400]
["2015-03-09",40,460.09999999999997]
["2015-03-10",52,538]
["2015-03-11",25,597.5]
["2015-03-12",42,389.15]
["2015-03-13",45,228]
["2015-03-14",31,627.5]
["2015-03-15",51,408]
["2015-03-16",34,339.7]
["2015-03-17",21,527.5]
["2015-03-18",55,364.5]
["2015-03-19",24,695]
["2015-03-20",42,462.25]
["2015-03-21",38,277.34999999999997]
["2015-03-22",48,334]
["2015-03-23",40,445.04999999999995]
["2015-03-24",45,496]
["2015-03-25",45,514]
["2015-03-26",46,350]
["2015-03-27",33,697.5]
["2015-03-28",37,258]
["2015-03-29",24,257.5]
["2015-03-30",36,313.89999999999998]
["2015-03-31",37,481.59999999999997]
["2015-04-01",47,594]
["2015-04-02",64,442.5]
["2015-04-03",60,397.5]
["2015-04-04",50,272]
["2015-04-05",62,208.5]
["2015-04-06",50,200]
["2015-04-07",62,301.5]
["2015-04-08",68,187]
["2015-04-09",65,150]
["2015-04-10",57,445.5]
["2015-04-11",53,498]
["2015-04-12",43,599.85]
["2015-04-13",39,393.45]
["2015-04-14",61,196.5]
["2015-04-15",42,457.95]
["2015-04-16",42,225.75]
["2015-04-17",52,598]
["2015-04-18",68,268]
["2015-04-19",50,468]
["2015-04-20",46,236]
["2015-04-21",58,415.5]
["2015-04-22",56,418.5]
["2015-04-23",61,279]
["2015-04-24",56,195]
["2015-04-25",61,325.5]
["2015-04-26",67,283]
["2015-04-27",68,281]
["2015-04-28",61,313.5]
["2015-04-29",52,466]
["2015-04-30",63,351]
["2015-05-01",80,226]
["2015-05-02",75,165]
["2015-05-03",51,532]
["2015-05-04",44,410]
["2015-05-05",79,121]
["2015-05-06",61,340.5]
["2015-05-07",49,264]
["2015-05-08",80,212]
["2015-05-09",47,590]
["2015-05-10",77,153]
["2015-05-11",60,202.5]
["2015-05-12",54,564]
["2015-05-13",44,326]
["2015-05-14",50,308]
["2015-05-15",69,220]
["2015-05-16",59,448.5]
["2015-05-17",81,239]
["2015-05-18",82,227]
["2015-05-19",59,222]
["2015-05-20",47,282]
["2015-05-21",71,225]
["2015-05-22",45,332]
["2015-05-23",65,178]
["2015-05-24",78,290]
["2015-05-25",54,246]
["2015-05-26",46,598]
["2015-05-27",81,186]
["2015-05-28",52,584]
["2015-05-29",79,259]
["2015-05-30",77,162]
["2015-05-31",66,162]
["2015-06-01",60,300]
["2015-06-02",66,228]
["2015-06-03",63,336]
["2015-06-04",76,164]
["2015-06-05",64,238.5]
["2015-06-06",54,548]
["2015-06-07",62,187.5]
["2015-06-08",75,208]
["2015-06-09",60,153]
["2015-06-10",61,343.5]
["2015-06-11",74,275]
["2015-06-12",72,138]
["2015-06-13",77,168]
["2015-06-14",56,289.5]
["2015-06-15",73,156]
["2015-06-16",60,345]
["2015-06-17",72,292]
["2015-06-18",66,100]
["2015-06-19",57,355.5]
["2015-06-20",57,171]
["2015-06-21",78,253]
["2015-06-22",56,303]
["2015-06-23",59,319.5]
["2015-06-24",71,115]
["2015-06-25",66,133]
["2015-06-26",58,318]
["2015-06-27",67,137]
["2015-06-28",66,275]
["2015-06-29",76,132]
["2015-06-30",57,244.5]
["2015-07-01",72,135]
["2015-07-02",62,246]
["2015-07-03",79,222]
["2015-07-04",83,297]
["2015-07-05",71,173]
["2015-07-06",78,154]
["2015-07-07",70,160]
["2015-07-08",81,139]
["2015-07-09",74,103]
["2015-07-10",92,508]
["2015-07-11",78,172]
["2015-07-12",87,429]
["2015-07-13",62,406.5]
["2015-07-14",85,340.5]
["2015-07-15",92,268]
["2015-07-16",84,235]
["2015-07-17",90,396]
["2015-07-18",66,254]
["2015-07-19",67,189]
["2015-07-20",79,293]
["2015-07-21",74,115]
["2015-07-22",84,181]
["2015-07-23",73,200]
["2015-07-24",72,234]
["2015-07-25",89,423]
["2015-07-26",75,102]
["2015-07-27",80,272]
["2015-07-28",65,159]
["2015-07-29",71,281]
["2015-07-30",91,210]
["2015-07-31",89,273]
["2015-08-01",98,310]
["2015-08-02",91,578]
["2015-08-03",82,300]
["2015-08-04",93,584]
["2015-08-05",73,159]
["2015-08-06",99,715]
["2015-08-07",85,426]
["2015-08-08",71,251]
["2015-08-09",90,294]
["2015-08-10",71,288]
["2015-08-11",97,740]
["2015-08-12",100,646.25]
["2015-08-13",96,337.5]
["2015-08-14",75,154]
["2015-08-15",80,153]
["2015-08-16",74,207]
["2015-08-17",84,151]
["2015-08-18",94,308]
["2015-08-19",99,285]
["2015-08-20",94,422]
["2015-08-21",95,400]
["2015-08-22",88,426]
["2015-08-23",83,182]
["2015-08-24",89,201]
["2015-08-25",79,294]
["2015-08-26",92,244]
["2015-08-27",100,379.5]
["2015-08-28",80,257]
["2015-08-29",97,590]
["2015-08-30",70,199]
["2015-08-31",90,248]
["2015-09-01",75,106]
["2015-09-02",86,217.5]
["2015-09-03",91,328]
["2015-09-04",80,133]
["2015-09-05",73,118]
["2015-09-06",85,442.5]
["2015-09-07",95,455]
["2015-09-08",75,188]
["2015-09-09",87,255]
["2015-09-10",80,294]
["2015-09-11",88,304.5]
["2015-09-12",75,138]
["2015-09-13",68,253]
["2015-09-14",70,180]
["2015-09-15",76,210]
["2015-09-16",87,436.5]
["2015-09-17",90,456]
["2015-09-18",67,273]
["2015-09-19",70,283]
["2015-09-20",97,345]
["2015-09-21",90,232]
["2015-09-22",86,301.5]
["2015-09-23",83,298]
["2015-09-24",69,283]
["2015-09-25",68,193]
["2015-09-26",95,742.5]
["2015-09-27",93,528]
["2015-09-28",79,204]
["2015-09-29",87,180]
["2015-09-30",79,230]
["2015-10-01",85,393]
["2015-10-02",89,310.5]
["2015-10-03",71,298]
["2015-10-04",58,424.5]
["2015-10-05",66,116]
["2015-10-06",82,288]
["2015-10-07",56,318]
["2015-10-08",81,291]
["2015-10-09",57,418.5]
["2015-10-10",59,159]
["2015-10-11",54,504]
["2015-10-12",88,231]
["2015-10-13",83,290]
["2015-10-14",85,303]
["2015-10-15",85,223.5]
["2015-10-16",81,300]
["2015-10-17",73,276]
["2015-10-18",71,142]
["2015-10-19",87,285]
["2015-10-20",86,396]
["2015-10-21",79,265]
["2015-10-22",61,151.5]
["2015-10-23",60,361.5]
["2015-10-24",78,210]
["2015-10-25",81,288]
["2015-10-26",79,174]
["2015-10-27",68,260]
["2015-10-28",68,197]
["2015-10-29",60,276]
["2015-10-30",73,137]
["2015-10-31",74,132]
["2015-11-01",46,224]
["2015-11-02",66,131]
["2015-11-03",50,210]
["2015-11-04",49,498]
["2015-11-05",68,284]
["2015-11-06",63,150]
["2015-11-07",57,289.5]
["2015-11-08",62,237]
["2015-11-09",66,136]
["2015-11-10",62,424.5]
["2015-11-11",53,574]
["2015-11-12",63,391.5]
["2015-11-13",61,382.5]
["2015-11-14",64,436.5]
["2015-11-15",51,578]
["2015-11-16",56,328.5]
["2015-11-17",54,590]
["2015-11-18",53,446]
["2015-11-19",50,226]
["2015-11-20",68,231]
["2015-11-21",58,315]
["2015-11-22",49,220]
["2015-11-23",67,264]
["2015-11-24",49,358]
["2015-11-25",47,498]
["2015-11-26",49,448]
["2015-11-27",47,578]
["2015-11-28",62,402]
["2015-11-29",47,466]
["2015-11-30",49,330]
["2015-12-01",64,214.5]
["2015-12-02",62,190.5]
["2015-12-03",60,270]
["2015-12-04",66,109]
["2015-12-05",62,384]
["2015-12-06",66,286]
["2015-12-07",49,278]
["2015-12-08",62,264]
["2015-12-09",65,179]
["2015-12-10",62,265.5]
["2015-12-11",61,357]
["2015-12-12",31,737.5]
["2015-12-13",53,272]
["2015-12-14",52,236]
["2015-12-15",55,267]
["2015-12-16",61,450]
["2015-12-17",50,486]
["2015-12-18",55,217.5]
["2015-12-19",60,274.5]
["2015-12-20",43,221.45]
["2015-12-21",63,174]
["2015-12-22",49,206]
["2015-12-23",40,539.65]
["2015-12-24",33,677.5]
["2015-12-25",62,157.5]
["2015-12-26",32,680]
["2015-12-27",33,580]
["2015-12-28",52,538]
["2015-12-29",32,252.5]
["2015-12-30",59,348]
["2015-12-31",32,387.5]

{"sheet":"2 Variable Xbar Lines (an)","index":78,"n_rows":438,"n_cols":9,"rows_kept":438,"cut":false,"hidden":false,"merged":[]}
["Is there an Association or Relationship Between 2 Variables?"]
["Scatter Plot with Trendline & X and Y Mean Lines"]
["Create Scatter Plot with Trendline & X and Y Mean Lines to divide"]
["chart into four quadrants in order to further define the pattern and"]
["relationship between the two variables"]
[]
["Example 1:","Relationship Between Temperature & Ice Cream Sales?"]
["Sample Point","x","y",null,"Xbar (F)","Y",null,"X","Ybar (IC Sales)"]
[null,null,null,null,{"$f":"B10","v":66.272727272727266},0,null,30,{"$f":"C10","v":4068.3636363636365}]
["Mean",{"$f":"AVERAGE(B13:B23)","v":66.272727272727266},{"$f":"AVERAGE(C13:C23)","v":4068.3636363636365},null,{"$f":"B10","v":66.272727272727266},10000,null,100,{"$f":"C10","v":4068.3636363636365}]
[]
["No.","Temperature (F)","Sales Ice Cream"]
[1,91,7113]
[2,45,2044]
[3,46,1108]
[4,83,7093]
[5,76,3902]
[6,96,6676]
[7,75,5403]
[8,42,886]
[9,70,4740]
[10,47,2637]
[11,58,3150]
[]
[]
[]
[]
[]
["Example 2:","Relationship Between Temperature & Chicken Soup Sales?"]
["Sample Point","x","y",null,"Xbar (F)","Y",null,"X","Ybar (CS Sales)"]
[null,null,null,null,{"$f":"B32","v":61.142857142857146},0,null,30,{"$f":"C32","v":5352.1428571428569}]
["Mean",{"$f":"AVERAGE(B35:B48)","v":61.142857142857146},{"$f":"AVERAGE(C35:C48)","v":5352.1428571428569},null,{"$f":"B32","v":61.142857142857146},10000,null,100,{"$f":"C32","v":5352.1428571428569}]
[]
["No.","Temperature (F)","Sales Chicken Soup"]
[1,86,3300]
[2,40,8200]
[3,41,8900]
[4,78,3100]
[5,71,4020]
[6,91,1950]
[7,70,2500]
[8,37,6500]
[9,65,6210]
[10,42,5250]
[11,53,7200]
[12,83,2750]
[13,63,7150]
[14,36,7900]
[15,43,6210]
[]
[]
["Example 4:","Relationship Between # of Years Using Excel & Expert Rating"]
["Sample Point","x","y",null,"Xbar (Years)","Y",null,"X","Ybar (Rating)"]
[null,null,null,null,{"$f":"B55","v":9},0,null,0,{"$f":"C55","v":5.3076923076923075}]
["Mean",{"$f":"AVERAGE(B58:B71)","v":9},{"$f":"AVERAGE(C58:C71)","v":5.3076923076923075},null,{"$f":"B55","v":9},12,null,22,{"$f":"C55","v":5.3076923076923075}]
[]
["No.","# Of Years Using Excel","Excel Expert Rating"]
[1,3,5]
[2,8,1]
[3,6,9]
[4,11,5]
[5,20,3]
[6,7,4]
[7,9,10]
[8,3,6]
[9,19,10]
[10,2,1]
[11,16,2]
[12,12,7]
[13,1,6]
[]
[]
[]
["Example 5:","Relationship Between Temperature and Energy Expense, relationship looks nonlinear: as x increases, y decreases for a while and then increases"]
["Sample Point","x","y",null,"Xbar (Temp)","Y",null,"X","Ybar (Energy)"]
[null,null,null,null,{"$f":"B77","v":62.389830508474574},0,null,0,{"$f":"C77","v":327.58149717514124}]
["Mean",{"$f":"AVERAGE(B85:B438)","v":62.389830508474574},{"$f":"AVERAGE(C85:C438)","v":327.58149717514124},null,{"$f":"B77","v":62.389830508474574},800,null,120,{"$f":"C77","v":327.58149717514124}]
[]
["Example 5:","Relationship Between Temperature and Energy Expense, relationship looks nonlinear: as x increases, y decreases for a while and then increases"]
[]
[]
["Sample Point","x","y"]
[]
["Date","Temperature X","Energy Expense Y"]
["2015-01-12",46,236]
["2015-01-13",52,304]
["2015-01-14",55,163.5]
["2015-01-15",46,214]
["2015-01-16",47,210]
["2015-01-17",50,508]
["2015-01-18",36,294.55]
["2015-01-19",47,250]
["2015-01-20",40,371.95]
["2015-01-21",46,478]
["2015-01-22",55,258]
["2015-01-23",40,559]
["2015-01-24",53,536]
["2015-01-25",44,576]
["2015-01-26",48,446]
["2015-01-27",46,300]
["2015-01-28",56,250.5]
["2015-01-29",36,412.79999999999995]
["2015-01-30",38,511.7]
["2015-01-31",37,311.75]
["2015-02-01",44,478]
["2015-02-02",30,282.5]
["2015-02-03",48,476]
["2015-02-04",37,565.44999999999993]
["2015-02-05",43,567.6]
["2015-02-06",39,634.25]
["2015-02-07",34,266.59999999999997]
["2015-02-08",33,345]
["2015-02-09",38,393.45]
["2015-02-10",38,567.6]
["2015-02-11",37,266.59999999999997]
["2015-02-12",35,503.09999999999997]
["2015-02-13",53,242]
["2015-02-14",55,216]
["2015-02-15",58,354]
["2015-02-16",41,432.15]
["2015-02-17",43,406.34999999999997]
["2015-02-18",43,627.79999999999995]
["2015-02-19",32,705]
["2015-02-20",53,534]
["2015-02-21",54,282]
["2015-02-22",56,435]
["2015-02-23",52,380]
["2015-02-24",57,372]
["2015-02-25",51,240]
["2015-02-26",40,346.15]
["2015-02-27",32,577.5]
["2015-02-28",34,406.34999999999997]
["2015-03-01",37,468.7]
["2015-03-02",39,483.75]
["2015-03-03",22,462.5]
["2015-03-04",38,408.5]
["2015-03-05",40,311.75]
["2015-03-06",51,420]
["2015-03-07",32,410]
["2015-03-08",45,400]
["2015-03-09",40,460.09999999999997]
["2015-03-10",52,538]
["2015-03-11",25,597.5]
["2015-03-12",42,389.15]
["2015-03-13",45,228]
["2015-03-14",31,627.5]
["2015-03-15",51,408]
["2015-03-16",34,339.7]
["2015-03-17",21,527.5]
["2015-03-18",55,364.5]
["2015-03-19",24,695]
["2015-03-20",42,462.25]
["2015-03-21",38,277.34999999999997]
["2015-03-22",48,334]
["2015-03-23",40,445.04999999999995]
["2015-03-24",45,496]
["2015-03-25",45,514]
["2015-03-26",46,350]
["2015-03-27",33,697.5]
["2015-03-28",37,258]
["2015-03-29",24,257.5]
["2015-03-30",36,313.89999999999998]
["2015-03-31",37,481.59999999999997]
["2015-04-01",47,594]
["2015-04-02",64,442.5]
["2015-04-03",60,397.5]
["2015-04-04",50,272]
["2015-04-05",62,208.5]
["2015-04-06",50,200]
["2015-04-07",62,301.5]
["2015-04-08",68,187]
["2015-04-09",65,150]
["2015-04-10",57,445.5]
["2015-04-11",53,498]
["2015-04-12",43,599.85]
["2015-04-13",39,393.45]
["2015-04-14",61,196.5]
["2015-04-15",42,457.95]
["2015-04-16",42,225.75]
["2015-04-17",52,598]
["2015-04-18",68,268]
["2015-04-19",50,468]
["2015-04-20",46,236]
["2015-04-21",58,415.5]
["2015-04-22",56,418.5]
["2015-04-23",61,279]
["2015-04-24",56,195]
["2015-04-25",61,325.5]
["2015-04-26",67,283]
["2015-04-27",68,281]
["2015-04-28",61,313.5]
["2015-04-29",52,466]
["2015-04-30",63,351]
["2015-05-01",80,226]
["2015-05-02",75,165]
["2015-05-03",51,532]
["2015-05-04",44,410]
["2015-05-05",79,121]
["2015-05-06",61,340.5]
["2015-05-07",49,264]
["2015-05-08",80,212]
["2015-05-09",47,590]
["2015-05-10",77,153]
["2015-05-11",60,202.5]
["2015-05-12",54,564]
["2015-05-13",44,326]
["2015-05-14",50,308]
["2015-05-15",69,220]
["2015-05-16",59,448.5]
["2015-05-17",81,239]
["2015-05-18",82,227]
["2015-05-19",59,222]
["2015-05-20",47,282]
["2015-05-21",71,225]
["2015-05-22",45,332]
["2015-05-23",65,178]
["2015-05-24",78,290]
["2015-05-25",54,246]
["2015-05-26",46,598]
["2015-05-27",81,186]
["2015-05-28",52,584]
["2015-05-29",79,259]
["2015-05-30",77,162]
["2015-05-31",66,162]
["2015-06-01",60,300]
["2015-06-02",66,228]
["2015-06-03",63,336]
["2015-06-04",76,164]
["2015-06-05",64,238.5]
["2015-06-06",54,548]
["2015-06-07",62,187.5]
["2015-06-08",75,208]
["2015-06-09",60,153]
["2015-06-10",61,343.5]
["2015-06-11",74,275]
["2015-06-12",72,138]
["2015-06-13",77,168]
["2015-06-14",56,289.5]
["2015-06-15",73,156]
["2015-06-16",60,345]
["2015-06-17",72,292]
["2015-06-18",66,100]
["2015-06-19",57,355.5]
["2015-06-20",57,171]
["2015-06-21",78,253]
["2015-06-22",56,303]
["2015-06-23",59,319.5]
["2015-06-24",71,115]
["2015-06-25",66,133]
["2015-06-26",58,318]
["2015-06-27",67,137]
["2015-06-28",66,275]
["2015-06-29",76,132]
["2015-06-30",57,244.5]
["2015-07-01",72,135]
["2015-07-02",62,246]
["2015-07-03",79,222]
["2015-07-04",83,297]
["2015-07-05",71,173]
["2015-07-06",78,154]
["2015-07-07",70,160]
["2015-07-08",81,139]
["2015-07-09",74,103]
["2015-07-10",92,508]
["2015-07-11",78,172]
["2015-07-12",87,429]
["2015-07-13",62,406.5]
["2015-07-14",85,340.5]
["2015-07-15",92,268]
["2015-07-16",84,235]
["2015-07-17",90,396]
["2015-07-18",66,254]
["2015-07-19",67,189]
["2015-07-20",79,293]
["2015-07-21",74,115]
["2015-07-22",84,181]
["2015-07-23",73,200]
["2015-07-24",72,234]
["2015-07-25",89,423]
["2015-07-26",75,102]
["2015-07-27",80,272]
["2015-07-28",65,159]
["2015-07-29",71,281]
["2015-07-30",91,210]
["2015-07-31",89,273]
["2015-08-01",98,310]
["2015-08-02",91,578]
["2015-08-03",82,300]
["2015-08-04",93,584]
["2015-08-05",73,159]
["2015-08-06",99,715]
["2015-08-07",85,426]
["2015-08-08",71,251]
["2015-08-09",90,294]
["2015-08-10",71,288]
["2015-08-11",97,740]
["2015-08-12",100,646.25]
["2015-08-13",96,337.5]
["2015-08-14",75,154]
["2015-08-15",80,153]
["2015-08-16",74,207]
["2015-08-17",84,151]
["2015-08-18",94,308]
["2015-08-19",99,285]
["2015-08-20",94,422]
["2015-08-21",95,400]
["2015-08-22",88,426]
["2015-08-23",83,182]
["2015-08-24",89,201]
["2015-08-25",79,294]
["2015-08-26",92,244]
["2015-08-27",100,379.5]
["2015-08-28",80,257]
["2015-08-29",97,590]
["2015-08-30",70,199]
["2015-08-31",90,248]
["2015-09-01",75,106]
["2015-09-02",86,217.5]
["2015-09-03",91,328]
["2015-09-04",80,133]
["2015-09-05",73,118]
["2015-09-06",85,442.5]
["2015-09-07",95,455]
["2015-09-08",75,188]
["2015-09-09",87,255]
["2015-09-10",80,294]
["2015-09-11",88,304.5]
["2015-09-12",75,138]
["2015-09-13",68,253]
["2015-09-14",70,180]
["2015-09-15",76,210]
["2015-09-16",87,436.5]
["2015-09-17",90,456]
["2015-09-18",67,273]
["2015-09-19",70,283]
["2015-09-20",97,345]
["2015-09-21",90,232]
["2015-09-22",86,301.5]
["2015-09-23",83,298]
["2015-09-24",69,283]
["2015-09-25",68,193]
["2015-09-26",95,742.5]
["2015-09-27",93,528]
["2015-09-28",79,204]
["2015-09-29",87,180]
["2015-09-30",79,230]
["2015-10-01",85,393]
["2015-10-02",89,310.5]
["2015-10-03",71,298]
["2015-10-04",58,424.5]
["2015-10-05",66,116]
["2015-10-06",82,288]
["2015-10-07",56,318]
["2015-10-08",81,291]
["2015-10-09",57,418.5]
["2015-10-10",59,159]
["2015-10-11",54,504]
["2015-10-12",88,231]
["2015-10-13",83,290]
["2015-10-14",85,303]
["2015-10-15",85,223.5]
["2015-10-16",81,300]
["2015-10-17",73,276]
["2015-10-18",71,142]
["2015-10-19",87,285]
["2015-10-20",86,396]
["2015-10-21",79,265]
["2015-10-22",61,151.5]
["2015-10-23",60,361.5]
["2015-10-24",78,210]
["2015-10-25",81,288]
["2015-10-26",79,174]
["2015-10-27",68,260]
["2015-10-28",68,197]
["2015-10-29",60,276]
["2015-10-30",73,137]
["2015-10-31",74,132]
["2015-11-01",46,224]
["2015-11-02",66,131]
["2015-11-03",50,210]
["2015-11-04",49,498]
["2015-11-05",68,284]
["2015-11-06",63,150]
["2015-11-07",57,289.5]
["2015-11-08",62,237]
["2015-11-09",66,136]
["2015-11-10",62,424.5]
["2015-11-11",53,574]
["2015-11-12",63,391.5]
["2015-11-13",61,382.5]
["2015-11-14",64,436.5]
["2015-11-15",51,578]
["2015-11-16",56,328.5]
["2015-11-17",54,590]
["2015-11-18",53,446]
["2015-11-19",50,226]
["2015-11-20",68,231]
["2015-11-21",58,315]
["2015-11-22",49,220]
["2015-11-23",67,264]
["2015-11-24",49,358]
["2015-11-25",47,498]
["2015-11-26",49,448]
["2015-11-27",47,578]
["2015-11-28",62,402]
["2015-11-29",47,466]
["2015-11-30",49,330]
["2015-12-01",64,214.5]
["2015-12-02",62,190.5]
["2015-12-03",60,270]
["2015-12-04",66,109]
["2015-12-05",62,384]
["2015-12-06",66,286]
["2015-12-07",49,278]
["2015-12-08",62,264]
["2015-12-09",65,179]
["2015-12-10",62,265.5]
["2015-12-11",61,357]
["2015-12-12",31,737.5]
["2015-12-13",53,272]
["2015-12-14",52,236]
["2015-12-15",55,267]
["2015-12-16",61,450]
["2015-12-17",50,486]
["2015-12-18",55,217.5]
["2015-12-19",60,274.5]
["2015-12-20",43,221.45]
["2015-12-21",63,174]
["2015-12-22",49,206]
["2015-12-23",40,539.65]
["2015-12-24",33,677.5]
["2015-12-25",62,157.5]
["2015-12-26",32,680]
["2015-12-27",33,580]
["2015-12-28",52,538]
["2015-12-29",32,252.5]
["2015-12-30",59,348]
["2015-12-31",32,387.5]

{"sheet":"2 Variables Cov and Corr","index":79,"n_rows":474,"n_cols":14,"rows_kept":474,"cut":false,"hidden":false,"merged":[]}
["Is there an Association or Relationship Between 2 Variables?"]
["Covariance"]
["Sample Covariance = sxy = (∑(xi - Xbar)*(yi - Ybar))/(n-1)"]
["Use COVARIANCE.S function"]
["Covariance measures the strength of the linear relationship between 2 variables, but has problems with units"]
["Positive values indicate a positive relationship; negative, a negative relationship"]
["Correlation Coefficient"]
["Sample Correlation Coefficient = rxy = sxy /(sx * sy)"]
["Use PEARSON or CORREL functions",null,null,null,null,null,null,null,null,null,"Xbar"," =","Sample Mean of x-values","\"Xbar\""]
["Correlation Coefficient measure the strength and direction of linear association or relationship",null,null,null,null,null,null,null,null,null,"xi"," =","Particular X Value","\"x sub i\""]
["Values range from -1 to +1",null,null,null,null,null,null,null,null,null,"Ybar"," =","Sample Mean of y-values","\"Ybar\""]
["Close to +1, positive or direct linear relationship, as x increases, y increases",null,null,null,null,null,null,null,null,null,"yi"," =","Particular Y Value","\"y sub i\""]
["Close to -1, negative or indirect linear relationship, as x increases, y decreases",null,null,null,null,null,null,null,null,null,"sxy"," =","Sample Covariance","\"s sub xy\""]
["Close to zero is weak linear relationship, but use Scatter Chart to see if the relationship looks nonlinear",null,null,null,null,null,null,null,null,null,"n"," =","count of records or observations for Sample","\"n\""]
["Correlation is not causation.",null,null,null,null,null,null,null,null,null,"sx"," =","Sample Standard Deviation of X Values","\"s sub x\""]
[null,null,null,null,null,null,null,null,null,null,"sy"," =","Sample Standard Deviation of Y Values","\"s sub y\""]
["Example 1:","Relationship Between Temperature & Ice Cream Sales?"]
["Sample Point","x","y"]
[]
["Mean"]
["SD = s"]
["Count -1"]
[]
["No.","Temperature (F)","Sales Ice Cream","(xi-Xbar)","(yi-Ybar)","(xi-Xbar)*(yi-Ybar)"]
[1,91,7113]
[2,45,2044]
[3,46,1108]
[4,83,7093]
[5,76,3902]
[6,96,6676]
[7,75,5403]
[8,42,886]
[9,70,4740]
[10,47,2637]
[11,58,3150]
[]
[null,null,null,null,"Total"]
[null,null,null,null,"Total / n-1",null,"Sample Covariance = Measures Strength of Relationship, but problem with units"]
[null,null,null,null,"Sample Covariance",null,"COVARIANCE.S"]
[null,null,null,null,"(Total / n-1)/(Sx*Sy)",null,"Coefficient of Correlation = Measures Strength and Direction Of Liner Relationship, Not causation."]
[null,null,null,null,"(Total / n-1)/(Sx*Sy)",null,"CORREL or PEARSON"]
[null,null,null,null,null,null,"r^2","Coefficient of Determination = R^2 = \"Goodness of fit for our line\" ",null,null,null,null,null,"How much of the variation in y can be explained by the variation in x"]
[null,null,null,null,null,null,"RSQ","Coefficient of Determination = R^2 = \"Goodness of fit for our line\" "]
["Example 2:","Relationship Between Temperature & Chicken Soup Sales?"]
[]
["sxy",null,"Sample Covariance = Measures Strength of Relationship, but problem with units"]
["rxy",null,"Coefficient of Correlation = Measures Strength and Direction Of Liner Relationship"]
[]
["No.","Temperature (F), X","Sales Chicken Soup, Y"]
[1,86,3300]
[2,40,8200]
[3,41,8900]
[4,78,3100]
[5,71,4020]
[6,91,1950]
[7,70,2500]
[8,37,6500]
[9,65,6210]
[10,42,5250]
[11,53,7200]
[12,83,2750]
[13,63,7150]
[14,36,7900]
[15,43,6210]
[]
[]
["Example 4:","Relationship Between # of Years Using Excel & Expert Rating"]
[]
["sxy",null,"Sample Covariance = Measures Strength of Relationship, but problem with units"]
["rxy",null,"Coefficient of Correlation = Measures Strength and Direction Of Liner Relationship"]
[]
["No.","# Of Years Using Excel","Excel Expert Rating"]
[1,3,5]
[2,8,1]
[3,6,9]
[4,11,5]
[5,20,3]
[6,7,4]
[7,9,10]
[8,3,6]
[9,19,10]
[10,2,1]
[11,16,2]
[12,12,7]
[13,1,6]
[]
[]
["Example 5:","Relationship Between Temperature and Energy Expense, relationship looks nonlinear: as x increases, y decreases for a while and then increases"]
[]
["sxy",null,"Sample Covariance = Measures Strength of Relationship, but problem with units"]
["rxy",null,"Coefficient of Correlation = Measures Strength and Direction Of Liner Relationship"]
[]
["Date","Temperature X","Energy Expense Y"]
["2015-01-12",46,236]
["2015-01-13",52,304]
["2015-01-14",55,163.5]
["2015-01-15",46,214]
["2015-01-16",47,210]
["2015-01-17",50,508]
["2015-01-18",36,294.55]
["2015-01-19",47,250]
["2015-01-20",40,371.95]
["2015-01-21",46,478]
["2015-01-22",55,258]
["2015-01-23",40,559]
["2015-01-24",53,536]
["2015-01-25",44,576]
["2015-01-26",48,446]
["2015-01-27",46,300]
["2015-01-28",56,250.5]
["2015-01-29",36,412.79999999999995]
["2015-01-30",38,511.7]
["2015-01-31",37,311.75]
["2015-02-01",44,478]
["2015-02-02",30,282.5]
["2015-02-03",48,476]
["2015-02-04",37,565.44999999999993]
["2015-02-05",43,567.6]
["2015-02-06",39,634.25]
["2015-02-07",34,266.59999999999997]
["2015-02-08",33,345]
["2015-02-09",38,393.45]
["2015-02-10",38,567.6]
["2015-02-11",37,266.59999999999997]
["2015-02-12",35,503.09999999999997]
["2015-02-13",53,242]
["2015-02-14",55,216]
["2015-02-15",58,354]
["2015-02-16",41,432.15]
["2015-02-17",43,406.34999999999997]
["2015-02-18",43,627.79999999999995]
["2015-02-19",32,705]
["2015-02-20",53,534]
["2015-02-21",54,282]
["2015-02-22",56,435]
["2015-02-23",52,380]
["2015-02-24",57,372]
["2015-02-25",51,240]
["2015-02-26",40,346.15]
["2015-02-27",32,577.5]
["2015-02-28",34,406.34999999999997]
["2015-03-01",37,468.7]
["2015-03-02",39,483.75]
["2015-03-03",22,462.5]
["2015-03-04",38,408.5]
["2015-03-05",40,311.75]
["2015-03-06",51,420]
["2015-03-07",32,410]
["2015-03-08",45,400]
["2015-03-09",40,460.09999999999997]
["2015-03-10",52,538]
["2015-03-11",25,597.5]
["2015-03-12",42,389.15]
["2015-03-13",45,228]
["2015-03-14",31,627.5]
["2015-03-15",51,408]
["2015-03-16",34,339.7]
["2015-03-17",21,527.5]
["2015-03-18",55,364.5]
["2015-03-19",24,695]
["2015-03-20",42,462.25]
["2015-03-21",38,277.34999999999997]
["2015-03-22",48,334]
["2015-03-23",40,445.04999999999995]
["2015-03-24",45,496]
["2015-03-25",45,514]
["2015-03-26",46,350]
["2015-03-27",33,697.5]
["2015-03-28",37,258]
["2015-03-29",24,257.5]
["2015-03-30",36,313.89999999999998]
["2015-03-31",37,481.59999999999997]
["2015-04-01",47,594]
["2015-04-02",64,442.5]
["2015-04-03",60,397.5]
["2015-04-04",50,272]
["2015-04-05",62,208.5]
["2015-04-06",50,200]
["2015-04-07",62,301.5]
["2015-04-08",68,187]
["2015-04-09",65,150]
["2015-04-10",57,445.5]
["2015-04-11",53,498]
["2015-04-12",43,599.85]
["2015-04-13",39,393.45]
["2015-04-14",61,196.5]
["2015-04-15",42,457.95]
["2015-04-16",42,225.75]
["2015-04-17",52,598]
["2015-04-18",68,268]
["2015-04-19",50,468]
["2015-04-20",46,236]
["2015-04-21",58,415.5]
["2015-04-22",56,418.5]
["2015-04-23",61,279]
["2015-04-24",56,195]
["2015-04-25",61,325.5]
["2015-04-26",67,283]
["2015-04-27",68,281]
["2015-04-28",61,313.5]
["2015-04-29",52,466]
["2015-04-30",63,351]
["2015-05-01",80,226]
["2015-05-02",75,165]
["2015-05-03",51,532]
["2015-05-04",44,410]
["2015-05-05",79,121]
["2015-05-06",61,340.5]
["2015-05-07",49,264]
["2015-05-08",80,212]
["2015-05-09",47,590]
["2015-05-10",77,153]
["2015-05-11",60,202.5]
["2015-05-12",54,564]
["2015-05-13",44,326]
["2015-05-14",50,308]
["2015-05-15",69,220]
["2015-05-16",59,448.5]
["2015-05-17",81,239]
["2015-05-18",82,227]
["2015-05-19",59,222]
["2015-05-20",47,282]
["2015-05-21",71,225]
["2015-05-22",45,332]
["2015-05-23",65,178]
["2015-05-24",78,290]
["2015-05-25",54,246]
["2015-05-26",46,598]
["2015-05-27",81,186]
["2015-05-28",52,584]
["2015-05-29",79,259]
["2015-05-30",77,162]
["2015-05-31",66,162]
["2015-06-01",60,300]
["2015-06-02",66,228]
["2015-06-03",63,336]
["2015-06-04",76,164]
["2015-06-05",64,238.5]
["2015-06-06",54,548]
["2015-06-07",62,187.5]
["2015-06-08",75,208]
["2015-06-09",60,153]
["2015-06-10",61,343.5]
["2015-06-11",74,275]
["2015-06-12",72,138]
["2015-06-13",77,168]
["2015-06-14",56,289.5]
["2015-06-15",73,156]
["2015-06-16",60,345]
["2015-06-17",72,292]
["2015-06-18",66,100]
["2015-06-19",57,355.5]
["2015-06-20",57,171]
["2015-06-21",78,253]
["2015-06-22",56,303]
["2015-06-23",59,319.5]
["2015-06-24",71,115]
["2015-06-25",66,133]
["2015-06-26",58,318]
["2015-06-27",67,137]
["2015-06-28",66,275]
["2015-06-29",76,132]
["2015-06-30",57,244.5]
["2015-07-01",72,135]
["2015-07-02",62,246]
["2015-07-03",79,222]
["2015-07-04",83,297]
["2015-07-05",71,173]
["2015-07-06",78,154]
["2015-07-07",70,160]
["2015-07-08",81,139]
["2015-07-09",74,103]
["2015-07-10",92,508]
["2015-07-11",78,172]
["2015-07-12",87,429]
["2015-07-13",62,406.5]
["2015-07-14",85,340.5]
["2015-07-15",92,268]
["2015-07-16",84,235]
["2015-07-17",90,396]
["2015-07-18",66,254]
["2015-07-19",67,189]
["2015-07-20",79,293]
["2015-07-21",74,115]
["2015-07-22",84,181]
["2015-07-23",73,200]
["2015-07-24",72,234]
["2015-07-25",89,423]
["2015-07-26",75,102]
["2015-07-27",80,272]
["2015-07-28",65,159]
["2015-07-29",71,281]
["2015-07-30",91,210]
["2015-07-31",89,273]
["2015-08-01",98,310]
["2015-08-02",91,578]
["2015-08-03",82,300]
["2015-08-04",93,584]
["2015-08-05",73,159]
["2015-08-06",99,715]
["2015-08-07",85,426]
["2015-08-08",71,251]
["2015-08-09",90,294]
["2015-08-10",71,288]
["2015-08-11",97,740]
["2015-08-12",100,646.25]
["2015-08-13",96,337.5]
["2015-08-14",75,154]
["2015-08-15",80,153]
["2015-08-16",74,207]
["2015-08-17",84,151]
["2015-08-18",94,308]
["2015-08-19",99,285]
["2015-08-20",94,422]
["2015-08-21",95,400]
["2015-08-22",88,426]
["2015-08-23",83,182]
["2015-08-24",89,201]
["2015-08-25",79,294]
["2015-08-26",92,244]
["2015-08-27",100,379.5]
["2015-08-28",80,257]
["2015-08-29",97,590]
["2015-08-30",70,199]
["2015-08-31",90,248]
["2015-09-01",75,106]
["2015-09-02",86,217.5]
["2015-09-03",91,328]
["2015-09-04",80,133]
["2015-09-05",73,118]
["2015-09-06",85,442.5]
["2015-09-07",95,455]
["2015-09-08",75,188]
["2015-09-09",87,255]
["2015-09-10",80,294]
["2015-09-11",88,304.5]
["2015-09-12",75,138]
["2015-09-13",68,253]
["2015-09-14",70,180]
["2015-09-15",76,210]
["2015-09-16",87,436.5]
["2015-09-17",90,456]
["2015-09-18",67,273]
["2015-09-19",70,283]
["2015-09-20",97,345]
["2015-09-21",90,232]
["2015-09-22",86,301.5]
["2015-09-23",83,298]
["2015-09-24",69,283]
["2015-09-25",68,193]
["2015-09-26",95,742.5]
["2015-09-27",93,528]
["2015-09-28",79,204]
["2015-09-29",87,180]
["2015-09-30",79,230]
["2015-10-01",85,393]
["2015-10-02",89,310.5]
["2015-10-03",71,298]
["2015-10-04",58,424.5]
["2015-10-05",66,116]
["2015-10-06",82,288]
["2015-10-07",56,318]
["2015-10-08",81,291]
["2015-10-09",57,418.5]
["2015-10-10",59,159]
["2015-10-11",54,504]
["2015-10-12",88,231]
["2015-10-13",83,290]
["2015-10-14",85,303]
["2015-10-15",85,223.5]
["2015-10-16",81,300]
["2015-10-17",73,276]
["2015-10-18",71,142]
["2015-10-19",87,285]
["2015-10-20",86,396]
["2015-10-21",79,265]
["2015-10-22",61,151.5]
["2015-10-23",60,361.5]
["2015-10-24",78,210]
["2015-10-25",81,288]
["2015-10-26",79,174]
["2015-10-27",68,260]
["2015-10-28",68,197]
["2015-10-29",60,276]
["2015-10-30",73,137]
["2015-10-31",74,132]
["2015-11-01",46,224]
["2015-11-02",66,131]
["2015-11-03",50,210]
["2015-11-04",49,498]
["2015-11-05",68,284]
["2015-11-06",63,150]
["2015-11-07",57,289.5]
["2015-11-08",62,237]
["2015-11-09",66,136]
["2015-11-10",62,424.5]
["2015-11-11",53,574]
["2015-11-12",63,391.5]
["2015-11-13",61,382.5]
["2015-11-14",64,436.5]
["2015-11-15",51,578]
["2015-11-16",56,328.5]
["2015-11-17",54,590]
["2015-11-18",53,446]
["2015-11-19",50,226]
["2015-11-20",68,231]
["2015-11-21",58,315]
["2015-11-22",49,220]
["2015-11-23",67,264]
["2015-11-24",49,358]
["2015-11-25",47,498]
["2015-11-26",49,448]
["2015-11-27",47,578]
["2015-11-28",62,402]
["2015-11-29",47,466]
["2015-11-30",49,330]
["2015-12-01",64,214.5]
["2015-12-02",62,190.5]
["2015-12-03",60,270]
["2015-12-04",66,109]
["2015-12-05",62,384]
["2015-12-06",66,286]
["2015-12-07",49,278]
["2015-12-08",62,264]
["2015-12-09",65,179]
["2015-12-10",62,265.5]
["2015-12-11",61,357]
["2015-12-12",31,737.5]
["2015-12-13",53,272]
["2015-12-14",52,236]
["2015-12-15",55,267]
["2015-12-16",61,450]
["2015-12-17",50,486]
["2015-12-18",55,217.5]
["2015-12-19",60,274.5]
["2015-12-20",43,221.45]
["2015-12-21",63,174]
["2015-12-22",49,206]
["2015-12-23",40,539.65]
["2015-12-24",33,677.5]
["2015-12-25",62,157.5]
["2015-12-26",32,680]
["2015-12-27",33,580]
["2015-12-28",52,538]
["2015-12-29",32,252.5]
["2015-12-30",59,348]
["2015-12-31",32,387.5]
[]
[]
[]
["Perfect +1"]
[]
["Correlation",{"$f":"PEARSON(B456:B462,A456:A462)","v":1}]
[]
["Units","Costs"]
[5,{"$f":"A456*15","v":75}]
[10,150]
[6,90]
[19,285]
[20,300]
[15,225]
[16,240]
[]
[]
["Perfect -1"]
[]
["Correlation",{"$f":"PEARSON(B470:B474,A470:A474)","v":-0.99999999999999989}]
[]
["Inspections","Defects"]
[25,5]
[{"$f":"A470-5","v":20},{"$f":"B470+5","v":10}]
[{"$f":"A471-5","v":15},{"$f":"B471+5","v":15}]
[{"$f":"A472-5","v":10},{"$f":"B472+5","v":20}]
[{"$f":"A473-5","v":5},{"$f":"B473+5","v":25}]

{"sheet":"2 Variables Cov and Corr (an)","index":80,"n_rows":474,"n_cols":14,"rows_kept":474,"cut":false,"hidden":false,"merged":[]}
["Is there an Association or Relationship Between 2 Variables?"]
["Covariance"]
["Sample Covariance = sxy = (∑(xi - Xbar)*(yi - Ybar))/(n-1)"]
["Use COVARIANCE.S function"]
["Covariance measures the strength of the linear relationship between 2 variables, but has problems with units"]
["Positive values indicate a positive relationship; negative, a negative relationship"]
["Correlation Coefficient"]
["Sample Correlation Coefficient = rxy = sxy /(sx * sy)"]
["Use PEARSON or CORREL functions",null,null,null,null,null,null,null,null,null,"Xbar"," =","Sample Mean of x-values","\"Xbar\""]
["Correlation Coefficient measure the strength and direction of linear association or relationship",null,null,null,null,null,null,null,null,null,"xi"," =","Particular X Value","\"x sub i\""]
["Values range from -1 to +1",null,null,null,null,null,null,null,null,null,"Ybar"," =","Sample Mean of y-values","\"Ybar\""]
["Close to +1, positive or direct linear relationship, as x increases, y increases",null,null,null,null,null,null,null,null,null,"yi"," =","Particular Y Value","\"y sub i\""]
["Close to -1, negative or indirect linear relationship, as x increases, y decreases",null,null,null,null,null,null,null,null,null,"sxy"," =","Sample Covariance","\"s sub xy\""]
["Close to zero is weak linear relationship, but use Scatter Chart to see if the relationship looks nonlinear",null,null,null,null,null,null,null,null,null,"n"," =","count of records or observations for Sample","\"n\""]
["Correlation is not causation.",null,null,null,null,null,null,null,null,null,"sx"," =","Sample Standard Deviation of X Values","\"s sub x\""]
[null,null,null,null,null,null,null,null,null,null,"sy"," =","Sample Standard Deviation of Y Values","\"s sub y\""]
["Example 1:","Relationship Between Temperature & Ice Cream Sales?"]
["Sample Point","x","y"]
[]
["Mean",{"$f":"AVERAGE(B25:B35)","v":66.272727272727266},{"$f":"AVERAGE(C25:C35)","v":4068.3636363636365}]
["SD = s",{"$f":"_xlfn.STDEV.S(B25:B35)","v":19.626975870423379},{"$f":"_xlfn.STDEV.S(C25:C35)","v":2309.9667215233758}]
["Count -1",{"$f":"COUNT(B25:B35)-1","v":10}]
[]
["No.","Temperature (F)","Sales Ice Cream","(xi-Xbar)","(yi-Ybar)","(xi-Xbar)*(yi-Ybar)"]
[1,91,7113,{"$f":"B25-$B$20","v":24.727272727272734},{"$f":"C25-$C$20","v":3044.6363636363635},{"$f":"E25*D25","v":75285.553719008283}]
[2,45,2044,{"$f":"B26-$B$20","v":-21.272727272727266},{"$f":"C26-$C$20","v":-2024.3636363636365},{"$f":"E26*D26","v":43063.73553719007}]
[3,46,1108,-20.272727272727266,-2960.3636363636365,60014.644628099159]
[4,83,7093,16.727272727272734,3024.6363636363635,50593.917355371916]
[5,76,3902,9.7272727272727337,-166.36363636363649,-1618.2644628099197]
[6,96,6676,29.727272727272734,2607.6363636363635,77517.917355371916]
[7,75,5403,8.7272727272727337,1334.6363636363635,11647.73553719009]
[8,42,886,-24.272727272727266,-3182.3636363636365,77244.644628099151]
[9,70,4740,3.7272727272727337,671.63636363636351,2503.3719008264502]
[10,47,2637,-19.272727272727266,-1431.3636363636365,27586.28099173553]
[11,58,3150,-8.2727272727272663,-918.36363636363649,7597.3719008264416]
[]
[null,null,null,null,"Total",{"$f":"SUM(F25:F35)","v":431436.90909090912}]
[null,null,null,null,"Total / n-1",{"$f":"F37/B22","v":43143.69090909091},"Sample Covariance = Measures Strength of Relationship, but problem with units"]
[null,null,null,null,"Sample Covariance",{"$f":"_xlfn.COVARIANCE.S(C25:C35,B25:B35)","v":43143.69090909091},"COVARIANCE.S"]
[null,null,null,null,"(Total / n-1)/(Sx*Sy)",{"$f":"F38/PRODUCT(B21:C21)","v":0.95160821837154441},"Coefficient of Correlation = Measures Strength and Direction Of Liner Relationship, Not causation."]
[null,null,null,null,"(Total / n-1)/(Sx*Sy)",{"$f":"PEARSON(C25:C35,B25:B35)","v":0.9516082183715443},"CORREL or PEARSON"]
[null,null,null,null,null,{"$f":"F41^2","v":0.90555820127226472},"r^2","Coefficient of Determination = R^2 = \"Goodness of fit for our line\" ",null,null,null,null,null,"How much of the variation in y can be explained by the variation in x"]
[null,null,null,null,null,{"$f":"RSQ(C25:C35,B25:B35)","v":0.90555820127226427},"RSQ","Coefficient of Determination = R^2 = \"Goodness of fit for our line\" "]
["Example 2:","Relationship Between Temperature & Chicken Soup Sales?"]
[]
["sxy",{"$f":"_xlfn.COVARIANCE.S(C50:C64,B50:B64)","v":-37874.333333333328},"Sample Covariance = Measures Strength of Relationship, but problem with units"]
["rxy",{"$f":"CORREL(C50:C64,B50:B64)","v":-0.84813245031454598},"Coefficient of Correlation = Measures Strength and Direction Of Liner Relationship"]
[]
["No.","Temperature (F), X","Sales Chicken Soup, Y"]
[1,86,3300]
[2,40,8200]
[3,41,8900]
[4,78,3100]
[5,71,4020]
[6,91,1950]
[7,70,2500]
[8,37,6500]
[9,65,6210]
[10,42,5250]
[11,53,7200]
[12,83,2750]
[13,63,7150]
[14,36,7900]
[15,43,6210]
[]
[]
["Example 4:","Relationship Between # of Years Using Excel & Expert Rating"]
[]
["sxy",{"$f":"_xlfn.COVARIANCE.S(C73:C85,B73:B85)","v":1.7500000000000002},"Sample Covariance = Measures Strength of Relationship, but problem with units"]
["rxy",{"$f":"PEARSON(C73:C85,B73:B85)","v":8.8517994535762423E-2},"Coefficient of Correlation = Measures Strength and Direction Of Liner Relationship"]
[]
["No.","# Of Years Using Excel","Excel Expert Rating"]
[1,3,5]
[2,8,1]
[3,6,9]
[4,11,5]
[5,20,3]
[6,7,4]
[7,9,10]
[8,3,6]
[9,19,10]
[10,2,1]
[11,16,2]
[12,12,7]
[13,1,6]
[]
[]
["Example 5:","Relationship Between Temperature and Energy Expense, relationship looks nonlinear: as x increases, y decreases for a while and then increases"]
[]
["sxy",{"$f":"_xlfn.COVARIANCE.S(C94:C447,B94:B447)","v":-878.32237000048053},"Sample Covariance = Measures Strength of Relationship, but problem with units"]
["rxy",{"$f":"PEARSON(C94:C447,B94:B447)","v":-0.34094297478401964},"Coefficient of Correlation = Measures Strength and Direction Of Liner Relationship"]
[]
["Date","Temperature X","Energy Expense Y"]
["2015-01-12",46,236]
["2015-01-13",52,304]
["2015-01-14",55,163.5]
["2015-01-15",46,214]
["2015-01-16",47,210]
["2015-01-17",50,508]
["2015-01-18",36,294.55]
["2015-01-19",47,250]
["2015-01-20",40,371.95]
["2015-01-21",46,478]
["2015-01-22",55,258]
["2015-01-23",40,559]
["2015-01-24",53,536]
["2015-01-25",44,576]
["2015-01-26",48,446]
["2015-01-27",46,300]
["2015-01-28",56,250.5]
["2015-01-29",36,412.79999999999995]
["2015-01-30",38,511.7]
["2015-01-31",37,311.75]
["2015-02-01",44,478]
["2015-02-02",30,282.5]
["2015-02-03",48,476]
["2015-02-04",37,565.44999999999993]
["2015-02-05",43,567.6]
["2015-02-06",39,634.25]
["2015-02-07",34,266.59999999999997]
["2015-02-08",33,345]
["2015-02-09",38,393.45]
["2015-02-10",38,567.6]
["2015-02-11",37,266.59999999999997]
["2015-02-12",35,503.09999999999997]
["2015-02-13",53,242]
["2015-02-14",55,216]
["2015-02-15",58,354]
["2015-02-16",41,432.15]
["2015-02-17",43,406.34999999999997]
["2015-02-18",43,627.79999999999995]
["2015-02-19",32,705]
["2015-02-20",53,534]
["2015-02-21",54,282]
["2015-02-22",56,435]
["2015-02-23",52,380]
["2015-02-24",57,372]
["2015-02-25",51,240]
["2015-02-26",40,346.15]
["2015-02-27",32,577.5]
["2015-02-28",34,406.34999999999997]
["2015-03-01",37,468.7]
["2015-03-02",39,483.75]
["2015-03-03",22,462.5]
["2015-03-04",38,408.5]
["2015-03-05",40,311.75]
["2015-03-06",51,420]
["2015-03-07",32,410]
["2015-03-08",45,400]
["2015-03-09",40,460.09999999999997]
["2015-03-10",52,538]
["2015-03-11",25,597.5]
["2015-03-12",42,389.15]
["2015-03-13",45,228]
["2015-03-14",31,627.5]
["2015-03-15",51,408]
["2015-03-16",34,339.7]
["2015-03-17",21,527.5]
["2015-03-18",55,364.5]
["2015-03-19",24,695]
["2015-03-20",42,462.25]
["2015-03-21",38,277.34999999999997]
["2015-03-22",48,334]
["2015-03-23",40,445.04999999999995]
["2015-03-24",45,496]
["2015-03-25",45,514]
["2015-03-26",46,350]
["2015-03-27",33,697.5]
["2015-03-28",37,258]
["2015-03-29",24,257.5]
["2015-03-30",36,313.89999999999998]
["2015-03-31",37,481.59999999999997]
["2015-04-01",47,594]
["2015-04-02",64,442.5]
["2015-04-03",60,397.5]
["2015-04-04",50,272]
["2015-04-05",62,208.5]
["2015-04-06",50,200]
["2015-04-07",62,301.5]
["2015-04-08",68,187]
["2015-04-09",65,150]
["2015-04-10",57,445.5]
["2015-04-11",53,498]
["2015-04-12",43,599.85]
["2015-04-13",39,393.45]
["2015-04-14",61,196.5]
["2015-04-15",42,457.95]
["2015-04-16",42,225.75]
["2015-04-17",52,598]
["2015-04-18",68,268]
["2015-04-19",50,468]
["2015-04-20",46,236]
["2015-04-21",58,415.5]
["2015-04-22",56,418.5]
["2015-04-23",61,279]
["2015-04-24",56,195]
["2015-04-25",61,325.5]
["2015-04-26",67,283]
["2015-04-27",68,281]
["2015-04-28",61,313.5]
["2015-04-29",52,466]
["2015-04-30",63,351]
["2015-05-01",80,226]
["2015-05-02",75,165]
["2015-05-03",51,532]
["2015-05-04",44,410]
["2015-05-05",79,121]
["2015-05-06",61,340.5]
["2015-05-07",49,264]
["2015-05-08",80,212]
["2015-05-09",47,590]
["2015-05-10",77,153]
["2015-05-11",60,202.5]
["2015-05-12",54,564]
["2015-05-13",44,326]
["2015-05-14",50,308]
["2015-05-15",69,220]
["2015-05-16",59,448.5]
["2015-05-17",81,239]
["2015-05-18",82,227]
["2015-05-19",59,222]
["2015-05-20",47,282]
["2015-05-21",71,225]
["2015-05-22",45,332]
["2015-05-23",65,178]
["2015-05-24",78,290]
["2015-05-25",54,246]
["2015-05-26",46,598]
["2015-05-27",81,186]
["2015-05-28",52,584]
["2015-05-29",79,259]
["2015-05-30",77,162]
["2015-05-31",66,162]
["2015-06-01",60,300]
["2015-06-02",66,228]
["2015-06-03",63,336]
["2015-06-04",76,164]
["2015-06-05",64,238.5]
["2015-06-06",54,548]
["2015-06-07",62,187.5]
["2015-06-08",75,208]
["2015-06-09",60,153]
["2015-06-10",61,343.5]
["2015-06-11",74,275]
["2015-06-12",72,138]
["2015-06-13",77,168]
["2015-06-14",56,289.5]
["2015-06-15",73,156]
["2015-06-16",60,345]
["2015-06-17",72,292]
["2015-06-18",66,100]
["2015-06-19",57,355.5]
["2015-06-20",57,171]
["2015-06-21",78,253]
["2015-06-22",56,303]
["2015-06-23",59,319.5]
["2015-06-24",71,115]
["2015-06-25",66,133]
["2015-06-26",58,318]
["2015-06-27",67,137]
["2015-06-28",66,275]
["2015-06-29",76,132]
["2015-06-30",57,244.5]
["2015-07-01",72,135]
["2015-07-02",62,246]
["2015-07-03",79,222]
["2015-07-04",83,297]
["2015-07-05",71,173]
["2015-07-06",78,154]
["2015-07-07",70,160]
["2015-07-08",81,139]
["2015-07-09",74,103]
["2015-07-10",92,508]
["2015-07-11",78,172]
["2015-07-12",87,429]
["2015-07-13",62,406.5]
["2015-07-14",85,340.5]
["2015-07-15",92,268]
["2015-07-16",84,235]
["2015-07-17",90,396]
["2015-07-18",66,254]
["2015-07-19",67,189]
["2015-07-20",79,293]
["2015-07-21",74,115]
["2015-07-22",84,181]
["2015-07-23",73,200]
["2015-07-24",72,234]
["2015-07-25",89,423]
["2015-07-26",75,102]
["2015-07-27",80,272]
["2015-07-28",65,159]
["2015-07-29",71,281]
["2015-07-30",91,210]
["2015-07-31",89,273]
["2015-08-01",98,310]
["2015-08-02",91,578]
["2015-08-03",82,300]
["2015-08-04",93,584]
["2015-08-05",73,159]
["2015-08-06",99,715]
["2015-08-07",85,426]
["2015-08-08",71,251]
["2015-08-09",90,294]
["2015-08-10",71,288]
["2015-08-11",97,740]
["2015-08-12",100,646.25]
["2015-08-13",96,337.5]
["2015-08-14",75,154]
["2015-08-15",80,153]
["2015-08-16",74,207]
["2015-08-17",84,151]
["2015-08-18",94,308]
["2015-08-19",99,285]
["2015-08-20",94,422]
["2015-08-21",95,400]
["2015-08-22",88,426]
["2015-08-23",83,182]
["2015-08-24",89,201]
["2015-08-25",79,294]
["2015-08-26",92,244]
["2015-08-27",100,379.5]
["2015-08-28",80,257]
["2015-08-29",97,590]
["2015-08-30",70,199]
["2015-08-31",90,248]
["2015-09-01",75,106]
["2015-09-02",86,217.5]
["2015-09-03",91,328]
["2015-09-04",80,133]
["2015-09-05",73,118]
["2015-09-06",85,442.5]
["2015-09-07",95,455]
["2015-09-08",75,188]
["2015-09-09",87,255]
["2015-09-10",80,294]
["2015-09-11",88,304.5]
["2015-09-12",75,138]
["2015-09-13",68,253]
["2015-09-14",70,180]
["2015-09-15",76,210]
["2015-09-16",87,436.5]
["2015-09-17",90,456]
["2015-09-18",67,273]
["2015-09-19",70,283]
["2015-09-20",97,345]
["2015-09-21",90,232]
["2015-09-22",86,301.5]
["2015-09-23",83,298]
["2015-09-24",69,283]
["2015-09-25",68,193]
["2015-09-26",95,742.5]
["2015-09-27",93,528]
["2015-09-28",79,204]
["2015-09-29",87,180]
["2015-09-30",79,230]
["2015-10-01",85,393]
["2015-10-02",89,310.5]
["2015-10-03",71,298]
["2015-10-04",58,424.5]
["2015-10-05",66,116]
["2015-10-06",82,288]
["2015-10-07",56,318]
["2015-10-08",81,291]
["2015-10-09",57,418.5]
["2015-10-10",59,159]
["2015-10-11",54,504]
["2015-10-12",88,231]
["2015-10-13",83,290]
["2015-10-14",85,303]
["2015-10-15",85,223.5]
["2015-10-16",81,300]
["2015-10-17",73,276]
["2015-10-18",71,142]
["2015-10-19",87,285]
["2015-10-20",86,396]
["2015-10-21",79,265]
["2015-10-22",61,151.5]
["2015-10-23",60,361.5]
["2015-10-24",78,210]
["2015-10-25",81,288]
["2015-10-26",79,174]
["2015-10-27",68,260]
["2015-10-28",68,197]
["2015-10-29",60,276]
["2015-10-30",73,137]
["2015-10-31",74,132]
["2015-11-01",46,224]
["2015-11-02",66,131]
["2015-11-03",50,210]
["2015-11-04",49,498]
["2015-11-05",68,284]
["2015-11-06",63,150]
["2015-11-07",57,289.5]
["2015-11-08",62,237]
["2015-11-09",66,136]
["2015-11-10",62,424.5]
["2015-11-11",53,574]
["2015-11-12",63,391.5]
["2015-11-13",61,382.5]
["2015-11-14",64,436.5]
["2015-11-15",51,578]
["2015-11-16",56,328.5]
["2015-11-17",54,590]
["2015-11-18",53,446]
["2015-11-19",50,226]
["2015-11-20",68,231]
["2015-11-21",58,315]
["2015-11-22",49,220]
["2015-11-23",67,264]
["2015-11-24",49,358]
["2015-11-25",47,498]
["2015-11-26",49,448]
["2015-11-27",47,578]
["2015-11-28",62,402]
["2015-11-29",47,466]
["2015-11-30",49,330]
["2015-12-01",64,214.5]
["2015-12-02",62,190.5]
["2015-12-03",60,270]
["2015-12-04",66,109]
["2015-12-05",62,384]
["2015-12-06",66,286]
["2015-12-07",49,278]
["2015-12-08",62,264]
["2015-12-09",65,179]
["2015-12-10",62,265.5]
["2015-12-11",61,357]
["2015-12-12",31,737.5]
["2015-12-13",53,272]
["2015-12-14",52,236]
["2015-12-15",55,267]
["2015-12-16",61,450]
["2015-12-17",50,486]
["2015-12-18",55,217.5]
["2015-12-19",60,274.5]
["2015-12-20",43,221.45]
["2015-12-21",63,174]
["2015-12-22",49,206]
["2015-12-23",40,539.65]
["2015-12-24",33,677.5]
["2015-12-25",62,157.5]
["2015-12-26",32,680]
["2015-12-27",33,580]
["2015-12-28",52,538]
["2015-12-29",32,252.5]
["2015-12-30",59,348]
["2015-12-31",32,387.5]
[]
[]
[]
["Perfect +1"]
[]
["Correlation",{"$f":"PEARSON(B456:B462,A456:A462)","v":1}]
[]
["Units","Costs"]
[5,{"$f":"A456*15","v":75}]
[10,150]
[6,90]
[19,285]
[20,300]
[15,225]
[16,240]
[]
[]
["Perfect -1"]
[]
["Correlation",{"$f":"PEARSON(B470:B474,A470:A474)","v":-0.99999999999999989}]
[]
["Inspections","Defects"]
[25,5]
[{"$f":"A470-5","v":20},{"$f":"B470+5","v":10}]
[{"$f":"A471-5","v":15},{"$f":"B471+5","v":15}]
[{"$f":"A472-5","v":10},{"$f":"B472+5","v":20}]
[{"$f":"A473-5","v":5},{"$f":"B473+5","v":25}]