{"text_format":"1","workbook":"xlsx","n_sheets":20,"sheets":["(1)","(2)","(3)","(4)","(7)","(10)","(11)","(15)","(19)","(21)","(27)","(30)","(33)","(33.2)","(51)","(52)","(53)","(54)","Case01(1)","Case01(2)"],"not_rendered":{"charts":17,"images":0,"macros":0,"external_links":0,"defined_names":2,"comments":1,"pivot_tables":23},"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"}

{"sheet":"(1)","index":0,"n_rows":7,"n_cols":4,"rows_kept":7,"cut":false,"hidden":false,"merged":[]}
["Green Cells Means that it has a formula"]
[]
["Categories or Classes","Frequency","Relative Frequency","% Frequency"]
["A",60,{"$f":"B4/B$7","v":0.5},{"$f":"C4","v":0.5}]
["B",24,{"$f":"B5/B$7","v":0.2},{"$f":"C5","v":0.2}]
["C",36,{"$f":"B6/B$7","v":0.3},{"$f":"C6","v":0.3}]
["Totals",{"$f":"SUM(B4:B6)","v":120},{"$f":"SUM(C4:C6)","v":1},{"$f":"SUM(D4:D6)","v":1}]

{"sheet":"(2)","index":1,"n_rows":8,"n_cols":4,"rows_kept":8,"cut":false,"hidden":false,"merged":[]}
["Green Cells Means that it has a formula"]
[]
["Categories or Classes","Frequency","Relative Frequency","% Frequency"]
["A",{"$f":"C4*B$8","v":44},0.22,{"$f":"B4/B$8","v":0.22}]
["B",{"$f":"C5*B$8","v":36},0.18,{"$f":"B5/B$8","v":0.18}]
["C",{"$f":"C6*B$8","v":80},0.4,{"$f":"B6/B$8","v":0.4}]
["D",{"$f":"C7*B$8","v":39.999999999999993},{"$f":"1-SUM(C4:C6)","v":0.19999999999999996},{"$f":"B7/B$8","v":0.19999999999999996}]
[null,200,{"$f":"SUM(C4:C7)","v":1},{"$f":"SUM(D4:D7)","v":1}]

{"sheet":"(3)","index":2,"n_rows":7,"n_cols":6,"rows_kept":7,"cut":false,"hidden":false,"merged":[]}
["Green Cells Means that it has a formula"]
[]
["Categories or Classes","Frequency","Relative Frequency","% Frequency","Degree #","Degree #"]
["Yes",58,{"$f":"B4/B$7","v":0.48333333333333334},{"$f":"C4","v":0.48333333333333334},{"$f":"360*D4","v":174},{"$f":"E4&CHAR(174)","v":"174®"}]
["No",42,{"$f":"B5/B$7","v":0.35},{"$f":"C5","v":0.35},{"$f":"360*D5","v":125.99999999999999},{"$f":"E5&CHAR(174)","v":"126®"}]
["No-opinion",20,{"$f":"B6/B$7","v":0.16666666666666666},{"$f":"C6","v":0.16666666666666666},{"$f":"360*D6","v":60},{"$f":"E6&CHAR(174)","v":"60®"}]
[null,{"$f":"SUM(B4:B6)","v":120},{"$f":"SUM(C4:C6)","v":0.99999999999999989},{"$f":"C7","v":0.99999999999999989},{"$f":"SUM(E4:E6)","v":360},{"$f":"E7&CHAR(174)","v":"360®"}]

{"sheet":"(4)","index":3,"n_rows":51,"n_cols":9,"rows_kept":51,"cut":false,"hidden":false,"merged":[]}
["Television Show",null,null,"Data is from Student Data Disc file \"BestTV.xlsx\""]
["Desperate Housewives",null,null,"Green Cells Means that it has a formula"]
["Without a Trace"]
["CSI",null,"a","Categorical",null,null,null,null,"c"]
["Law & Order",null,"d","CSI has the largest viewing audience and Desperate Housewives has the second highest."]
["CSI"]
["Desperate Housewives",null,"b","Television Show (Category or Class)","Frequency","Relative Frequency","% Frequency"]
["Desperate Housewives",null,null,"CSI",{"$f":"COUNTIF($A$2:$A$51,D8)","v":18},{"$f":"E8/E$12","v":0.36},{"$f":"F8","v":0.36}]
["Law & Order",null,null,"Desperate Housewives",{"$f":"COUNTIF($A$2:$A$51,D9)","v":13},{"$f":"E9/E$12","v":0.26},{"$f":"F9","v":0.26}]
["Law & Order",null,null,"Law & Order",{"$f":"COUNTIF($A$2:$A$51,D10)","v":10},{"$f":"E10/E$12","v":0.2},{"$f":"F10","v":0.2}]
["CSI",null,null,"Without a Trace",{"$f":"COUNTIF($A$2:$A$51,D11)","v":9},{"$f":"E11/E$12","v":0.18},{"$f":"F11","v":0.18}]
["CSI",null,null,null,{"$f":"SUM(E8:E11)","v":50},{"$f":"SUM(F8:F11)","v":1},{"$f":"F12","v":1}]
["CSI"]
["Desperate Housewives",null,null,"or"]
["Law & Order"]
["Desperate Housewives",null,"b","Television Show","Frequency"]
["Without a Trace",null,null,"CSI",18]
["CSI",null,null,"Desperate Housewives",13]
["CSI",null,null,"Law & Order",10]
["CSI",null,null,"Without a Trace",9]
["Desperate Housewives",null,null,"Grand Total",50]
["Desperate Housewives"]
["Law & Order"]
["Without a Trace",null,"c"]
["Law & Order"]
["Desperate Housewives"]
["CSI"]
["CSI"]
["Without a Trace"]
["CSI"]
["Without a Trace"]
["CSI"]
["Without a Trace"]
["CSI"]
["CSI"]
["Law & Order"]
["Without a Trace"]
["Law & Order"]
["Without a Trace"]
["CSI"]
["Without a Trace"]
["Law & Order"]
["CSI"]
["Desperate Housewives"]
["Desperate Housewives"]
["CSI"]
["Desperate Housewives"]
["CSI"]
["Desperate Housewives"]
["Desperate Housewives"]
["Law & Order"]

{"sheet":"(7)","index":4,"n_rows":58,"n_cols":7,"rows_kept":58,"cut":false,"hidden":false,"merged":[]}
["Green Cells Means that it has a formula"]
[]
["Ratings",null,"Rating (Category)",null,"Frequency","Relative Frequency","% Frequency"]
["E",null,"Excellent",{"$f":"LEFT(C4,1)","v":"E"},{"$f":"COUNTIF($A$4:$A$53,D4)","v":20},{"$f":"E4/E$9","v":0.4},{"$f":"F4","v":0.4}]
["E",null,"Very Good",{"$f":"LEFT(C5,1)","v":"V"},{"$f":"COUNTIF($A$4:$A$53,D5)","v":23},{"$f":"E5/E$9","v":0.46},0.46]
["V",null,"Good",{"$f":"LEFT(C6,1)","v":"G"},{"$f":"COUNTIF($A$4:$A$53,D6)","v":4},{"$f":"E6/E$9","v":0.08},0.08]
["G",null,"Fair",{"$f":"LEFT(C7,1)","v":"F"},{"$f":"COUNTIF($A$4:$A$53,D7)","v":1},{"$f":"E7/E$9","v":0.02},0.02]
["E",null,"Poor",{"$f":"LEFT(C8,1)","v":"P"},{"$f":"COUNTIF($A$4:$A$53,D8)","v":2},{"$f":"E8/E$9","v":0.04},0.04]
["E",null,null,null,{"$f":"SUM(E4:E8)","v":50},{"$f":"SUM(F4:F8)","v":1},1]
["G"]
["V"]
["E",null,"Row Labels","Count of Ratings"]
["E",null,"O",19]
["G",null,"V",13]
["V",null,"G",10]
["V",null,"A",6]
["V",null,"P",2]
["V",null,"Grand Total",50]
["V"]
["E",null,"a","Overall satisfaction is quite good. "]
["F",null,"b","Yes absolutely. In particular the Fair and"]
["E",null,null,"Poor respondents could say why they were"]
["V",null,null,"not satisfied. That information could be"]
["V",null,null,"used to try and make improvements."]
["E"]
["V"]
["V"]
["E"]
["E"]
["V"]
["E"]
["E"]
["P"]
["V"]
["E"]
["V"]
["V"]
["E"]
["V"]
["E"]
["V"]
["V"]
["E"]
["V"]
["E"]
["E"]
["V"]
["V"]
["V"]
["E"]
["G"]
["V"]
["P"]
["E"]
["V"]
["E"]
["V"]
["V"]

{"sheet":"(10)","index":5,"n_rows":1016,"n_cols":5,"rows_kept":1016,"cut":false,"hidden":false,"merged":[]}
["USA Rating",null,"USA Rating","Frequency","% Frequency"]
["Fair",null,"Excellent",{"$f":"COUNTIF($A$2:$A$1016,C2)","v":20},{"$f":"D2/$D$8","v":2.2371364653243849E-2}]
["Fair",null,"Good",{"$f":"COUNTIF($A$2:$A$1016,C3)","v":101},{"$f":"D3/$D$8","v":0.11297539149888143}]
["Fair",null,"Fair",{"$f":"COUNTIF($A$2:$A$1016,C4)","v":528},{"$f":"D4/$D$8","v":0.59060402684563762}]
["Fair",null,"Bad",{"$f":"COUNTIF($A$2:$A$1016,C5)","v":244},{"$f":"D5/$D$8","v":0.27293064876957496}]
["Fair",null,"Terrible",122,{"$f":"D6/$D$8","v":0.13646532438478748}]
["Fair"]
["Bad",null,"Total",{"$f":"SUM(D4:D6)","v":894},{"$f":"SUM(E4:E6)","v":1}]
["Bad"]
["Fair",null,"Spain Rating","Frequency","% Frequency"]
["Fair",null,"Excellent",0,{"$f":"D11/$D$17","v":0}]
["Terrible",null,"Good",4,{"$f":"D12/$D$17","v":0.04}]
["Bad",null,"Fair",46,0.46]
["Fair",null,"Bad",40,0.4]
["Terrible",null,"Terrible",10,0.1]
["Bad"]
["Fair",null,"Total",{"$f":"SUM(D11:D15)","v":100},{"$f":"SUM(E13:E15)","v":0.96000000000000008}]
["Fair"]
["Excellent"]
["Fair",null,"d","In the poll people in the united states think the Fed is doing a fair"]
["Bad",null,null,"job (59%), with more people thinking it is bad and terrible"]
["Bad",null,null,"tha good and excellent."]
["Good",null,"e","The Spain poll shows that people think that the ECB is doing fair"]
["Fair",null,null,"(46%), with many more people saying that they are doing bad and"]
["Excellent",null,null,"terrible than good and excellent. In fact, in the Spain poll, 9% said"]
["Bad",null,null,"that the ECB is doing Excellent. In general, the people"]
["Bad"]
["Fair"]
["Terrible"]
["Good"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Terrible"]
["Fair"]
["Terrible"]
["Bad"]
["Terrible"]
["Fair"]
["Good"]
["Fair"]
["Bad"]
["Good"]
["Fair"]
["Fair"]
["Bad"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Bad"]
["Bad"]
["Fair"]
["Fair"]
["Bad"]
["Bad"]
["Good"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Bad"]
["Bad"]
["Fair"]
["Bad"]
["Bad"]
["Fair"]
["Terrible"]
["Fair"]
["Excellent"]
["Fair"]
["Fair"]
["Fair"]
["Terrible"]
["Bad"]
["Fair"]
["Fair"]
["Good"]
["Fair"]
["Fair"]
["Bad"]
["Fair"]
["Fair"]
["Fair"]
["Good"]
["Fair"]
["Fair"]
["Fair"]
["Bad"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Terrible"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Bad"]
["Fair"]
["Good"]
["Fair"]
["Bad"]
["Good"]
["Terrible"]
["Fair"]
["Terrible"]
["Terrible"]
["Good"]
["Bad"]
["Fair"]
["Fair"]
["Good"]
["Good"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Terrible"]
["Fair"]
["Fair"]
["Bad"]
["Good"]
["Bad"]
["Fair"]
["Bad"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Good"]
["Good"]
["Terrible"]
["Fair"]
["Bad"]
["Bad"]
["Fair"]
["Bad"]
["Bad"]
["Fair"]
["Terrible"]
["Fair"]
["Excellent"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Bad"]
["Fair"]
["Fair"]
["Fair"]
["Terrible"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Terrible"]
["Bad"]
["Good"]
["Good"]
["Fair"]
["Bad"]
["Bad"]
["Fair"]
["Bad"]
["Terrible"]
["Bad"]
["Excellent"]
["Bad"]
["Bad"]
["Bad"]
["Bad"]
["Fair"]
["Terrible"]
["Bad"]
["Fair"]
["Fair"]
["Good"]
["Fair"]
["Bad"]
["Fair"]
["Bad"]
["Bad"]
["Fair"]
["Excellent"]
["Bad"]
["Terrible"]
["Fair"]
["Terrible"]
["Terrible"]
["Fair"]
["Good"]
["Fair"]
["Bad"]
["Terrible"]
["Terrible"]
["Bad"]
["Fair"]
["Terrible"]
["Good"]
["Fair"]
["Fair"]
["Bad"]
["Fair"]
["Bad"]
["Bad"]
["Terrible"]
["Bad"]
["Bad"]
["Bad"]
["Fair"]
["Terrible"]
["Excellent"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Bad"]
["Fair"]
["Fair"]
["Fair"]
["Bad"]
["Fair"]
["Bad"]
["Excellent"]
["Fair"]
["Good"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Bad"]
["Bad"]
["Fair"]
["Bad"]
["Terrible"]
["Fair"]
["Fair"]
["Bad"]
["Bad"]
["Bad"]
["Bad"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Terrible"]
["Fair"]
["Terrible"]
["Bad"]
["Fair"]
["Fair"]
["Bad"]
["Excellent"]
["Good"]
["Bad"]
["Fair"]
["Terrible"]
["Fair"]
["Bad"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Bad"]
["Terrible"]
["Fair"]
["Fair"]
["Bad"]
["Fair"]
["Bad"]
["Fair"]
["Bad"]
["Fair"]
["Terrible"]
["Bad"]
["Fair"]
["Excellent"]
["Fair"]
["Terrible"]
["Fair"]
["Terrible"]
["Bad"]
["Good"]
["Bad"]
["Fair"]
["Good"]
["Terrible"]
["Fair"]
["Fair"]
["Bad"]
["Bad"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Good"]
["Bad"]
["Terrible"]
["Bad"]
["Terrible"]
["Fair"]
["Fair"]
["Terrible"]
["Fair"]
["Fair"]
["Good"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Terrible"]
["Fair"]
["Terrible"]
["Bad"]
["Bad"]
["Fair"]
["Bad"]
["Fair"]
["Fair"]
["Bad"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Bad"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Bad"]
["Fair"]
["Fair"]
["Fair"]
["Bad"]
["Bad"]
["Terrible"]
["Fair"]
["Fair"]
["Good"]
["Bad"]
["Terrible"]
["Terrible"]
["Fair"]
["Terrible"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Terrible"]
["Fair"]
["Terrible"]
["Fair"]
["Fair"]
["Fair"]
["Good"]
["Fair"]
["Bad"]
["Fair"]
["Bad"]
["Good"]
["Fair"]
["Good"]
["Fair"]
["Terrible"]
["Terrible"]
["Bad"]
["Fair"]
["Good"]
["Fair"]
["Fair"]
["Fair"]
["Bad"]
["Terrible"]
["Good"]
["Fair"]
["Good"]
["Bad"]
["Good"]
["Fair"]
["Fair"]
["Fair"]
["Bad"]
["Bad"]
["Good"]
["Bad"]
["Terrible"]
["Terrible"]
["Terrible"]
["Fair"]
["Terrible"]
["Bad"]
["Fair"]
["Terrible"]
["Bad"]
["Fair"]
["Bad"]
["Fair"]
["Fair"]
["Terrible"]
["Fair"]
["Fair"]
["Bad"]
["Fair"]
["Bad"]
["Fair"]
["Bad"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Terrible"]
["Fair"]
["Fair"]
["Bad"]
["Excellent"]
["Fair"]
["Terrible"]
["Fair"]
["Bad"]
["Terrible"]
["Good"]
["Bad"]
["Bad"]
["Fair"]
["Terrible"]
["Bad"]
["Fair"]
["Excellent"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Good"]
["Fair"]
["Fair"]
["Fair"]
["Bad"]
["Terrible"]
["Bad"]
["Bad"]
["Terrible"]
["Fair"]
["Fair"]
["Bad"]
["Fair"]
["Excellent"]
["Bad"]
["Fair"]
["Bad"]
["Fair"]
["Fair"]
["Fair"]
["Terrible"]
["Good"]
["Bad"]
["Bad"]
["Bad"]
["Fair"]
["Fair"]
["Bad"]
["Fair"]
["Good"]
["Fair"]
["Terrible"]
["Fair"]
["Fair"]
["Bad"]
["Terrible"]
["Bad"]
["Bad"]
["Bad"]
["Fair"]
["Fair"]
["Fair"]
["Bad"]
["Bad"]
["Bad"]
["Terrible"]
["Fair"]
["Fair"]
["Fair"]
["Good"]
["Bad"]
["Good"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Terrible"]
["Fair"]
["Fair"]
["Bad"]
["Fair"]
["Good"]
["Fair"]
["Bad"]
["Fair"]
["Good"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Bad"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Terrible"]
["Fair"]
["Fair"]
["Bad"]
["Bad"]
["Fair"]
["Terrible"]
["Terrible"]
["Bad"]
["Bad"]
["Fair"]
["Bad"]
["Fair"]
["Fair"]
["Fair"]
["Bad"]
["Good"]
["Bad"]
["Bad"]
["Fair"]
["Terrible"]
["Bad"]
["Fair"]
["Fair"]
["Terrible"]
["Bad"]
["Fair"]
["Terrible"]
["Bad"]
["Fair"]
["Terrible"]
["Fair"]
["Fair"]
["Bad"]
["Excellent"]
["Fair"]
["Good"]
["Fair"]
["Bad"]
["Fair"]
["Bad"]
["Terrible"]
["Bad"]
["Fair"]
["Bad"]
["Fair"]
["Fair"]
["Bad"]
["Fair"]
["Good"]
["Fair"]
["Fair"]
["Fair"]
["Good"]
["Bad"]
["Fair"]
["Fair"]
["Fair"]
["Bad"]
["Excellent"]
["Bad"]
["Fair"]
["Fair"]
["Terrible"]
["Good"]
["Terrible"]
["Bad"]
["Fair"]
["Fair"]
["Bad"]
["Fair"]
["Fair"]
["Fair"]
["Good"]
["Terrible"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Bad"]
["Good"]
["Fair"]
["Fair"]
["Fair"]
["Bad"]
["Fair"]
["Fair"]
["Terrible"]
["Bad"]
["Bad"]
["Bad"]
["Fair"]
["Fair"]
["Fair"]
["Good"]
["Bad"]
["Fair"]
["Bad"]
["Fair"]
["Bad"]
["Fair"]
["Good"]
["Fair"]
["Good"]
["Fair"]
["Terrible"]
["Fair"]
["Bad"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Bad"]
["Good"]
["Bad"]
["Bad"]
["Good"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Good"]
["Bad"]
["Fair"]
["Fair"]
["Fair"]
["Bad"]
["Bad"]
["Terrible"]
["Fair"]
["Fair"]
["Fair"]
["Terrible"]
["Good"]
["Fair"]
["Terrible"]
["Good"]
["Terrible"]
["Terrible"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Excellent"]
["Good"]
["Fair"]
["Terrible"]
["Fair"]
["Bad"]
["Fair"]
["Fair"]
["Bad"]
["Fair"]
["Fair"]
["Fair"]
["Good"]
["Bad"]
["Fair"]
["Bad"]
["Fair"]
["Bad"]
["Fair"]
["Fair"]
["Terrible"]
["Fair"]
["Good"]
["Fair"]
["Terrible"]
["Fair"]
["Fair"]
["Bad"]
["Bad"]
["Fair"]
["Fair"]
["Bad"]
["Good"]
["Good"]
["Fair"]
["Fair"]
["Bad"]
["Bad"]
["Good"]
["Fair"]
["Fair"]
["Good"]
["Good"]
["Bad"]
["Fair"]
["Bad"]
["Terrible"]
["Bad"]
["Fair"]
["Good"]
["Good"]
["Fair"]
["Fair"]
["Terrible"]
["Fair"]
["Excellent"]
["Bad"]
["Bad"]
["Fair"]
["Fair"]
["Terrible"]
["Fair"]
["Fair"]
["Good"]
["Bad"]
["Fair"]
["Bad"]
["Fair"]
["Fair"]
["Bad"]
["Fair"]
["Bad"]
["Fair"]
["Fair"]
["Terrible"]
["Bad"]
["Good"]
["Bad"]
["Bad"]
["Fair"]
["Good"]
["Bad"]
["Fair"]
["Bad"]
["Fair"]
["Good"]
["Bad"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Terrible"]
["Fair"]
["Fair"]
["Bad"]
["Bad"]
["Bad"]
["Fair"]
["Bad"]
["Good"]
["Bad"]
["Bad"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Bad"]
["Good"]
["Good"]
["Fair"]
["Fair"]
["Fair"]
["Terrible"]
["Terrible"]
["Bad"]
["Bad"]
["Bad"]
["Good"]
["Bad"]
["Fair"]
["Fair"]
["Fair"]
["Bad"]
["Fair"]
["Excellent"]
["Fair"]
["Bad"]
["Bad"]
["Bad"]
["Fair"]
["Terrible"]
["Fair"]
["Terrible"]
["Terrible"]
["Fair"]
["Terrible"]
["Fair"]
["Good"]
["Good"]
["Bad"]
["Fair"]
["Good"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Bad"]
["Terrible"]
["Terrible"]
["Fair"]
["Bad"]
["Good"]
["Bad"]
["Terrible"]
["Bad"]
["Fair"]
["Fair"]
["Terrible"]
["Fair"]
["Good"]
["Good"]
["Terrible"]
["Terrible"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Terrible"]
["Terrible"]
["Bad"]
["Fair"]
["Bad"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Bad"]
["Fair"]
["Good"]
["Bad"]
["Fair"]
["Good"]
["Fair"]
["Fair"]
["Fair"]
["Good"]
["Bad"]
["Fair"]
["Terrible"]
["Good"]
["Fair"]
["Fair"]
["Bad"]
["Fair"]
["Bad"]
["Good"]
["Fair"]
["Good"]
["Terrible"]
["Fair"]
["Fair"]
["Bad"]
["Bad"]
["Terrible"]
["Bad"]
["Excellent"]
["Fair"]
["Good"]
["Fair"]
["Bad"]
["Fair"]
["Fair"]
["Fair"]
["Terrible"]
["Bad"]
["Fair"]
["Fair"]
["Fair"]
["Bad"]
["Fair"]
["Good"]
["Fair"]
["Bad"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Terrible"]
["Fair"]
["Bad"]
["Terrible"]
["Bad"]
["Good"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Bad"]
["Fair"]
["Bad"]
["Bad"]
["Terrible"]
["Terrible"]
["Fair"]
["Fair"]
["Fair"]
["Bad"]
["Fair"]
["Bad"]
["Bad"]
["Terrible"]
["Fair"]
["Good"]
["Terrible"]
["Fair"]
["Fair"]
["Excellent"]
["Terrible"]
["Fair"]
["Good"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Good"]
["Fair"]
["Fair"]
["Good"]
["Good"]
["Bad"]
["Fair"]
["Good"]
["Fair"]
["Bad"]
["Fair"]
["Fair"]
["Terrible"]
["Terrible"]
["Fair"]
["Bad"]
["Bad"]
["Bad"]
["Fair"]
["Fair"]
["Terrible"]
["Fair"]
["Bad"]
["Bad"]
["Fair"]
["Fair"]
["Fair"]
["Good"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Bad"]
["Bad"]
["Fair"]
["Fair"]
["Bad"]
["Fair"]
["Fair"]
["Good"]
["Fair"]
["Fair"]
["Good"]
["Fair"]
["Bad"]
["Terrible"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Fair"]
["Good"]
["Fair"]
["Fair"]
["Fair"]

{"sheet":"(11)","index":6,"n_rows":43,"n_cols":12,"rows_kept":43,"cut":false,"hidden":false,"merged":[]}
["Integer Data"]
[]
["Data",null,"a","Data","Frequency",null,"lower","upper","Class","Frequency","Relative Frequency","% Frequency"]
[14,null,null,"12-14",2,null,12,{"$f":"G4+$G$12","v":14},{"$f":"G4&\" - \"&H4","v":"12 - 14"},{"$f":"COUNTIFS($A$4:$A$43,\">=\"&G4,$A$4:$A$43,\"<=\"&H4)","v":2},{"$f":"J4/$J$9","v":0.05},{"$f":"K4","v":0.05}]
[19,null,null,"15-17",8,null,{"$f":"H4+1","v":15},{"$f":"G5+$G$12","v":17},"15 - 17",8,{"$f":"J5/$J$9","v":0.2},0.2]
[24,null,null,"18-20",11,null,{"$f":"H5+1","v":18},20,"18 - 20",11,{"$f":"J6/$J$9","v":0.27500000000000002},0.27500000000000002]
[19,null,null,"21-23",10,null,21,23,"21 - 23",10,{"$f":"J7/$J$9","v":0.25},0.25]
[16,null,null,"24-26",9,null,24,26,"24 - 26",9,{"$f":"J8/$J$9","v":0.22500000000000001},0.22500000000000001]
[20,null,null,"Grand Total",40,null,null,null,"Total",{"$f":"SUM(J4:J8)","v":40},{"$f":"SUM(K4:K8)","v":1},1]
[24]
[20,null,"b","Data","% Frequency",null,"increment"]
[21,null,null,"12-14",0.05,null,2]
[22,null,null,"15-17",0.2]
[24,null,null,"18-20",0.27500000000000002]
[18,null,null,"21-23",0.25]
[17,null,null,"24-26",0.22500000000000001]
[23,null,null,"Grand Total",1]
[26]
[22,null,"b","Data","Relative Frequency (Proportion)"]
[23,null,null,"12-14",0.05]
[25,null,null,"15-17",0.2]
[25,null,null,"18-20",0.27500000000000002]
[19,null,null,"21-23",0.25]
[18,null,null,"24-26",0.22500000000000001]
[16,null,null,"Grand Total",1]
[15]
[24]
[21]
[16]
[19]
[21]
[23]
[20]
[22]
[22]
[16]
[16]
[16]
[12]
[25]
[19]
[24]
[20]

{"sheet":"(15)","index":7,"n_rows":24,"n_cols":10,"rows_kept":24,"cut":false,"hidden":false,"merged":[]}
["Integer Data"]
[]
["Wait Time",null,"a-d",null,"Values"]
[2,null,null,"Wait Time","Frequency","Relative Frequency","Cumulative Frequency","% Cumulative"]
[5,null,null,"0-4",4,0.2,4,0.2]
[10,null,null,"5-9",8,0.4,12,0.6]
[12,null,null,"10-14",5,0.25,17,0.85]
[4,null,null,"15-19",2,0.1,19,0.95]
[4,null,null,"20-24",1,0.05,20,1]
[5,null,null,"Grand Total",20,1]
[17]
[11,null,null,"e","0.6 of the patients needing emergency service get that service in 9 or fewer minutes"]
[8]
[9]
[8,null,"a-d","lower","upper","Class","Frequency","Relative Frequency","Cumulative Frequency","% Cumulative"]
[12,null,null,0,{"$f":"D16+$D$24","v":4},{"$f":"D16&\" - \"&E16","v":"0 - 4"},{"$f":"COUNTIFS($A$4:$A$23,\">=\"&D16,$A$4:$A$23,\"<=\"&E16)","v":4},{"$f":"G16/$G$21","v":0.2},{"$f":"SUM($G$16:G16)","v":4},{"$f":"I16/$I$20","v":0.2}]
[21,null,null,{"$f":"E16+1","v":5},{"$f":"D17+$D$24","v":9},"5 - 9",8,{"$f":"G17/$G$21","v":0.4},{"$f":"SUM($G$16:G17)","v":12},{"$f":"I17/$I$20","v":0.6}]
[6,null,null,{"$f":"E17+1","v":10},14,"10 - 14",5,0.25,{"$f":"SUM($G$16:G18)","v":17},0.85]
[8,null,null,15,19,"15 - 19",2,0.1,{"$f":"SUM($G$16:G19)","v":19},0.95]
[7,null,null,20,24,"20 - 24",1,0.05,{"$f":"SUM($G$16:G20)","v":20},1]
[13,null,null,null,null,"Total",{"$f":"SUM(G16:G20)","v":20},{"$f":"SUM(H16:H20)","v":1}]
[18]
[3,null,null,"increment:"]
[null,null,null,4]

{"sheet":"(19)","index":8,"n_rows":53,"n_cols":12,"rows_kept":53,"cut":false,"hidden":false,"merged":[]}
[]
[]
["Corporation","Revenue ($B)",null,"Revenue","Frequency","Relative Frequency","Cum Frequency","% Cum Frequency"]
["Exxon Mobil",443,null,"0-49",6,0.12,6,0.12]
["Walmart",406,null,"50-99",29,0.57999999999999996,35,0.7]
["Chevron",263,null,"100-149",11,0.22,46,0.92]
["ConocoPhillips",231,null,"150-199",0,0,46,0.92]
["General Electric",149,null,"200-249",1,0.02,47,0.94]
["Ford Motors",146,null,"250-299",1,0.02,48,0.96]
["AT&T",124,null,"300-349",0,0,48,0.96]
["Hewlett-Packard",118,null,"350-399",0,0,48,0.96]
["Valero Energy",118,null,"400-449",2,0.04,50,1]
["Bank of America",113,null,"Grand Total",50,1]
["Citigroup",112]
["Berkshire Hathaway",108,null,"Histogram of 50 Largest Corp's Revenue in billions $"]
["IBM",104,null,"Upper","Lower","Class","Frequency","Relative Frequency","Cum Frequency","% Cum Frequency"]
["McKesson",102,null,0,{"$f":"D17+$D$28","v":49},{"$f":"D17&\" - \"&E17","v":"0 - 49"},{"$f":"COUNTIFS($B$4:$B$53,\">=\"&D17,$B$4:$B$53,\"<=\"&E17)","v":6},{"$f":"G17/$G$26","v":0.12},{"$f":"SUM($G$17:G17)","v":6},{"$f":"SUM($H$17:H17)","v":0.12},null,{"$f":"I17/$I$25","v":0.12}]
["JPMorgan Chase",101,null,{"$f":"E17+1","v":50},99,{"$f":"D18&\" - \"&E18","v":"50 - 99"},29,0.57999999999999996,{"$f":"SUM($G$17:G18)","v":35},{"$f":"SUM($H$17:H18)","v":0.7},null,0.7]
["Verizon ",97,null,{"$f":"E18+1","v":100},149,"100 - 149",11,0.22,{"$f":"SUM($G$17:G19)","v":46},{"$f":"SUM($H$17:H19)","v":0.91999999999999993},null,0.92]
["Cardinal Health",91,null,150,199,"150 - 199",0,0,{"$f":"SUM($G$17:G20)","v":46},{"$f":"SUM($H$17:H20)","v":0.91999999999999993},null,0.92]
["CVS Caremark",87,null,200,249,"200 - 249",1,0.02,{"$f":"SUM($G$17:G21)","v":47},{"$f":"SUM($H$17:H21)","v":0.94},null,0.94]
["Procter & Gamble",84,null,250,299,"250 - 299",1,0.02,{"$f":"SUM($G$17:G22)","v":48},{"$f":"SUM($H$17:H22)","v":0.96},null,0.96]
["UnitedHealth Group",81,null,300,349,"300 - 349",0,0,{"$f":"SUM($G$17:G23)","v":48},{"$f":"SUM($H$17:H23)","v":0.96},null,0.96]
["Kroger",76,null,350,399,"350 - 399",0,0,{"$f":"SUM($G$17:G24)","v":48},{"$f":"SUM($H$17:H24)","v":0.96},null,0.96]
["Marathon Oil",74,null,400,449,"400 - 449",2,0.04,{"$f":"SUM($G$17:G25)","v":50},{"$f":"SUM($H$17:H25)","v":1},null,1]
["Costco Wholesale",72,null,null,null,"Total",{"$f":"SUM(G17:G25)","v":50},{"$f":"SUM(H17:H25)","v":1}]
["Amerisource Bergen",71,null,"increnment:"]
["Home Depot",71,null,49]
["Archer Daniels Midland",70]
["Target",65,null,"e","Most of the companies make between 50 and 100 billion in revenue and 92% of the companies make 149 billion or less."]
["Johnson & Johnson",64,null,"f","The distribution is concentrated between 0 and 149 billion in revenues. The distribution has a very strong positive skew because a few companies have very high revenues"]
["Morgan Stanley",62,null,"g","The largest corp by revenue in 2010 was Exxon Mobile"]
["Boeing",61]
["Dell",61,null,null,"Max =",{"$f":"MAX(B4:B53)","v":443}]
["State Farm Insurance",61,null,null,null,{"$f":"INDEX($A$4:$A$53,MATCH(F34,$B$4:$B$53,0))","v":"Exxon Mobil"}]
["WellPoint",61]
["Microsoft",60]
["United Technologies",59]
["Walgreen",59]
["Dow Chemical",58]
["MetLife",55]
["Goldman Sachs",54]
["Sunoco",52]
["Wells Fargo",52]
["Caterpillar",51]
["Medco Health",51]
["United Parcel Service",51]
["Lowe's",48]
["Pfizer",48]
["Sears Holdings",47]
["Time Warner",47]
["Safeway",44]
["Pepsico",43]

{"sheet":"(21)","index":9,"n_rows":54,"n_cols":8,"rows_kept":54,"cut":false,"hidden":false,"merged":[]}
["Deciaml Data"]
["Personal Computer Use per week"]
[]
["Hours",null,"Step 1: Determine Number of Classes using the guide: 2^k > n"]
[4.0999999999999996,null,"COUNT function = No. in Sample = n =",{"$f":"COUNT(A5:A54)","v":50}]
[3.1,null,"k = No. Classes =",6,"This is a judgment call (the art side of statistics)"]
[4.0999999999999996,null,"2^7 =",{"$f":"2^D6","v":64}]
[10.8,null,"Step 2: Determine width of Classes"]
[7.2,null,"MAX",{"$f":"MAX(A5:A54)","v":14.8}]
[1.5,null,"MIN",{"$f":"MIN(A5:A54)","v":0.7}]
[4.8,null,"\"Range of ages\"",{"$f":"D9-D10","v":14.100000000000001}]
[4.0999999999999996,null,"Approximate width =",{"$f":"(D9-D10)/D6","v":2.35}]
[2.8,null,"Width of Class (Increment for each class)",3,"This is a judgment call (the art side of statistics)"]
[6.1,null,"\"Range for classes\" = ClassWidth*k =",{"$f":"D13*D6","v":18}]
[10.4,null,"\"Range for classes\" > \"Range of ages\"?",{"$f":"D14>D11","v":true},"Must be bigger than \"Range of ages\""]
[2,null,"Step 3: Determine Class Limits"]
[8.8000000000000007,null,"Lower Limit of First Class (look at MIN then estimate)",0,"This is a judgment call (the art side of statistics)"]
[9.5]
[5.7,null,"upper","lower","Class","Frequency","Relative Frequency","% Cumulative Frequency"]
[5.9,null,0,{"$f":"C20+$D$13","v":3},{"$f":"C20&\" <= Hours < \"&D20","v":"0 <= Hours < 3"},{"$f":"COUNTIFS($A$5:$A$54,\">=\"&C20,$A$5:$A$54,\"<\"&D20)","v":5},{"$f":"F20/$F$25","v":0.1},{"$f":"SUM($G$20:G20)","v":0.1}]
[14.8,null,{"$f":"D20","v":3},{"$f":"C21+$D$13","v":6},{"$f":"C21&\" <= Hours < \"&D21","v":"3 <= Hours < 6"},{"$f":"COUNTIFS($A$5:$A$54,\">=\"&C21,$A$5:$A$54,\"<\"&D21)","v":28},0.56000000000000005,{"$f":"SUM($G$20:G21)","v":0.66}]
[5.6,null,{"$f":"D21","v":6},{"$f":"C22+$D$13","v":9},"6 <= Hours < 9",8,0.16,{"$f":"SUM($G$20:G22)","v":0.82000000000000006}]
[12.9,null,{"$f":"D22","v":9},{"$f":"C23+$D$13","v":12},"9 <= Hours < 12",6,0.12,{"$f":"SUM($G$20:G23)","v":0.94000000000000006}]
[5.9,null,{"$f":"D23","v":12},{"$f":"C24+$D$13","v":15},"12 <= Hours < 15",3,0.06,{"$f":"SUM($G$20:G24)","v":1}]
[3.4,null,null,null,"Total",{"$f":"SUM(F20:F24)","v":50},{"$f":"SUM(G20:G24)","v":1}]
[5.4]
[4.3]
[12.1,null,null,null,"Ogive Book Data Points"]
[4.7]
[5.7]
[4.2]
[3.3]
[0.7]
[3.9]
[1.6]
[3.9]
[7.1]
[4]
[3.7]
[6.1]
[4.0999999999999996]
[10.3]
[9.1999999999999993]
[3.1]
[3]
[11.1]
[6.2]
[4.4000000000000004]
[6.1]
[3.7]
[3.5]
[7.6]
[5.7]
[3.1]

{"sheet":"(27)","index":10,"n_rows":16,"n_cols":7,"rows_kept":16,"cut":false,"hidden":false,"merged":[]}
["Resort","Location","Daily Rate",null,null,"MIN",{"$f":"MIN(C2:C16)","v":75}]
["BC","CL",137,null,null,"MAX",{"$f":"MAX(C2:C16)","v":145}]
["DV","UT",115]
["DP","CA",95,null,"a",7,{"$f":"REPT(\" 0 \",COUNTIF($C$2:$C$16,F4*10+0))&REPT(\" 1 \",COUNTIF($C$2:$C$16,F4*10+1))&REPT(\" 2 \",COUNTIF($C$2:$C$16,F4*10+2))&REPT(\" 3 \",COUNTIF($C$2:$C$16,F4*10+3))&REPT(\" 4 \",COUNTIF($C$2:$C$16,F4*10+4))&REPT(\" 5 \",COUNTIF($C$2:$C$16,F4*10+5))&REPT(\" 6 \",COUNTIF($C$2:$C$16,F4*10+6))&REPT(\" 7 \",COUNTIF($C$2:$C$16,F4*10+7))&REPT(\" 8 \",COUNTIF($C$2:$C$16,F4*10+8))&REPT(\" 9 \",COUNTIF($C$2:$C$16,F4*10+9))","v":" 5  9 "}]
["Hea","CA",145,null,null,8," 3  6 "]
["Hun","NY",79,null,null,9," 5  6  8 "]
["Mam","CA",111,null,null,10," 0  4  4 "]
["MS","NH",96,null,null,11," 1  5 "]
["MB","OR",83,null,null,12,""]
["Oke","VT",86,null,null,13," 7 "]
["PC","UT",145,null,null,14," 5  5 "]
["But","MA",75,null,null,15,""]
["SB","CO",98]
["ST","VT",104,null,"b","Observations such as the following can be made using the stem-and-leaf display.\n• The daily rate varies from $75 to $145\n• Typical mid-priced daily rates are $95 to $115 with the average daily rate around $100.\n\n• A daily rate in excess of $115 should be considered relatively high.  High daily rates of $137 and $145 were found at three ski resorts.\n"]
["SB","CA",100]
["WB","BC",104]

{"sheet":"(30)","index":11,"n_rows":24,"n_cols":5,"rows_kept":24,"cut":false,"hidden":false,"merged":[]}
["Observation","x","y",null,"a"]
[1,-22,22]
[2,-33,49]
[3,2,8]
[4,29,-16]
[5,-13,10]
[6,21,-28]
[7,-13,27]
[8,-23,35]
[9,14,-5]
[10,3,-3]
[11,-37,48]
[12,34,-29]
[13,9,-18]
[14,-33,31]
[15,20,-16]
[16,-3,14]
[17,-15,18]
[18,12,17]
[19,-20,-11]
[20,-7,-22]
[]
[]
["b","There is a negative relationship between x and y; y decreases as x increases."]

{"sheet":"(33)","index":12,"n_rows":18,"n_cols":26,"rows_kept":18,"cut":false,"hidden":false,"merged":[]}
[]
["Male Golfers",null,null,null,null,"Female Golfers",null,null,null,null,"Male Golfers",null,null,null,null,"Female Golfers",null,null,"Male Golfers",null,null,null,"Female Golfers"]
[null,"Green Conditions",null,null,null,null,"Green Conditions",null,null,null,null,"Green Conditions",null,null,null,"Green Conditions",null,null,null,"Green Conditions",null,null,null,"Green Conditions"]
["Handicap","Too Fast","Fine",null,null,"Handicap","Too Fast","Fine",null,null,"Handicap","Too Fast","Fine",null,"Handicap","Too Fast","Fine",null,"Handicap","Too Fast","Fine",null,"Handicap","Too Fast","Fine"]
["Under 15",10,40,{"$f":"SUM(B5:C5)","v":50},null,"Under 15",1,9,{"$f":"SUM(G5:H5)","v":10},null,"Under 15",{"$f":"B5/B$7","v":0.2857142857142857},{"$f":"C5/C$7","v":0.61538461538461542},null,"Under 15",{"$f":"G5/G$7","v":2.5000000000000001E-2},{"$f":"H5/H$7","v":0.15},null,"Under 15",{"$f":"B5/$D5","v":0.2},{"$f":"C5/$D5","v":0.8},{"$f":"SUM(T5:U5)","v":1},"Under 15",{"$f":"G5/$I5","v":0.1},{"$f":"H5/$I5","v":0.9},{"$f":"SUM(X5:Y5)","v":1}]
["15 or more",25,25,{"$f":"SUM(B6:C6)","v":50},null,"15 or more",39,51,{"$f":"SUM(G6:H6)","v":90},null,"15 or more",{"$f":"B6/B$7","v":0.7142857142857143},{"$f":"C6/C$7","v":0.38461538461538464},null,"15 or more",{"$f":"G6/G$7","v":0.97499999999999998},{"$f":"H6/H$7","v":0.85},null,"15 or more",{"$f":"B6/$D6","v":0.5},{"$f":"C6/$D6","v":0.5},{"$f":"SUM(T6:U6)","v":1},"15 or more",{"$f":"G6/$I6","v":0.43333333333333335},{"$f":"H6/$I6","v":0.56666666666666665},{"$f":"SUM(X6:Y6)","v":1}]
[null,{"$f":"SUM(B5:B6)","v":35},{"$f":"SUM(C5:C6)","v":65},{"$f":"SUM(B7:C7)","v":100},null,null,{"$f":"SUM(G5:G6)","v":40},{"$f":"SUM(H5:H6)","v":60},{"$f":"SUM(G7:H7)","v":100},null,null,{"$f":"SUM(L5:L6)","v":1},{"$f":"SUM(M5:M6)","v":1},null,null,{"$f":"SUM(P5:P6)","v":1},{"$f":"SUM(Q5:Q6)","v":1}]
[]
[null,"Green Conditions",null,null,null,null,"Green Conditions",null,null,null,null,"Green Conditions"]
["Gender","Too Fast","Fine",null,null,"Gender","Too Fast","Fine",null,null,"Gender","Too Fast","Fine"]
["Male",{"$f":"SUM(B5:B6)","v":35},{"$f":"SUM(C5:C6)","v":65},{"$f":"SUM(B11:C11)","v":100},null,"Male",{"$f":"B11/B$13","v":0.46666666666666667},{"$f":"C11/C$13","v":0.52},null,null,"Male",{"$f":"B11/$D11","v":0.35},{"$f":"C11/$D11","v":0.65},{"$f":"SUM(L11:M11)","v":1}]
["Female",{"$f":"SUM(G5:G6)","v":40},{"$f":"SUM(H5:H6)","v":60},{"$f":"SUM(B12:C12)","v":100},null,"Female",{"$f":"B12/B$13","v":0.53333333333333333},{"$f":"C12/C$13","v":0.48},null,null,"Female",{"$f":"B12/$D12","v":0.4},{"$f":"C12/$D12","v":0.6},{"$f":"SUM(L12:M12)","v":1}]
[null,{"$f":"SUM(B11:B12)","v":75},{"$f":"SUM(C11:C12)","v":125},{"$f":"SUM(B13:C13)","v":200},null,null,{"$f":"SUM(G11:G12)","v":1},{"$f":"SUM(H11:H12)","v":1},null,null,null,{"$f":"SUM(L11:L12)","v":0.75},{"$f":"SUM(M11:M12)","v":1.25}]
[]
["a","When the variables are aggregated, 40% of the females say that the greens are too fast compared to 35% of the men who say the greens are too fast. "]
["b","Amongst the better players, 20% (10/50) of the men say that the greens are too fast, whereas 10% (1/10) of the females say that the greens are too fast. This makes the better men players look like complainers. "]
["c","Amongst the players with higher handicaps, the men players are split 50%/50% between Too Fast and Fine, whereas the Female players show 43% too fast and 57% Fine. The gender with the highest percentage that say the Greens are too fast is male, at 50%."]
["d","If you look at the aggregated data, you do not see the Handicap variable and there are  40% of the females that say the greens are too fast, whereas 35% of the males say the greens are too fast. It is not until you show the Handicap variable in the unaggregated cross tabulations that you can see that amongst the lower handicapped players 20% of the men said it was too fast compared to 10% of the women, and amongst the higher handicapped players 50% of the men said it was too fast compared to 43% of the women. This is Simpson's paradox: Aggregated data, the Females are higher than males, whereas with the unaggregated data, the men are higher at both levels."]

{"sheet":"(33.2)","index":13,"n_rows":201,"n_cols":9,"rows_kept":201,"cut":false,"hidden":false,"merged":[[24,4,28,8]]}
["Male/Female","GreenConditions","Handicap"]
["Male","TooFast","Under15"]
["Male","TooFast","Under15",null,"Count of Handicap",null,"GreenConditions"]
["Male","TooFast","Under15",null,"Male/Female","Handicap","Fine","TooFast","Grand Total"]
["Male","TooFast","Under15",null,"Female","15OrMore",51,39,90]
["Male","TooFast","Under15",null,null,"Under15",9,1,10]
["Male","TooFast","Under15",null,"Female Total",null,60,40,100]
["Male","TooFast","Under15",null,"Male","15OrMore",25,25,50]
["Male","TooFast","Under15",null,null,"Under15",40,10,50]
["Male","TooFast","Under15",null,"Male Total",null,65,35,100]
["Male","TooFast","Under15",null,"Grand Total",null,125,75,200]
["Male","Fine","Under15"]
["Male","Fine","Under15"]
["Male","Fine","Under15"]
["Male","Fine","Under15",null,"Count of Handicap",null,"GreenConditions"]
["Male","Fine","Under15",null,"Male/Female","Handicap","Fine","TooFast","Grand Total"]
["Male","Fine","Under15",null,"Female","15OrMore",0.56666666666666665,0.43333333333333335,1]
["Male","Fine","Under15",null,null,"Under15",0.9,0.1,1]
["Male","Fine","Under15",null,"Female Total",null,0.6,0.4,1]
["Male","Fine","Under15",null,"Male","15OrMore",0.5,0.5,1]
["Male","Fine","Under15",null,null,"Under15",0.8,0.2,1]
["Male","Fine","Under15",null,"Male Total",null,0.65,0.35,1]
["Male","Fine","Under15",null,"Grand Total",null,0.625,0.375,1]
["Male","Fine","Under15"]
["Male","Fine","Under15",null,"Simpson's Paradox: 40/100 Females said greens too fast; 35/100 Males said greens too fast. But when we add the handicap variable we can see that 1/10 is less than 10/50 and 39/90 is less than 25/50 -- Both Female \"Green Too Fast\" are smaller than male's."]
["Male","Fine","Under15"]
["Male","Fine","Under15"]
["Male","Fine","Under15"]
["Male","Fine","Under15"]
["Male","Fine","Under15"]
["Male","Fine","Under15"]
["Male","Fine","Under15"]
["Male","Fine","Under15"]
["Male","Fine","Under15"]
["Male","Fine","Under15"]
["Male","Fine","Under15"]
["Male","Fine","Under15"]
["Male","Fine","Under15"]
["Male","Fine","Under15"]
["Male","Fine","Under15"]
["Male","Fine","Under15"]
["Male","Fine","Under15"]
["Male","Fine","Under15"]
["Male","Fine","Under15"]
["Male","Fine","Under15"]
["Male","Fine","Under15"]
["Male","Fine","Under15"]
["Male","Fine","Under15"]
["Male","Fine","Under15"]
["Male","Fine","Under15"]
["Male","Fine","Under15"]
["Male","TooFast","15OrMore"]
["Male","TooFast","15OrMore"]
["Male","TooFast","15OrMore"]
["Male","TooFast","15OrMore"]
["Male","TooFast","15OrMore"]
["Male","TooFast","15OrMore"]
["Male","TooFast","15OrMore"]
["Male","TooFast","15OrMore"]
["Male","TooFast","15OrMore"]
["Male","TooFast","15OrMore"]
["Male","TooFast","15OrMore"]
["Male","TooFast","15OrMore"]
["Male","TooFast","15OrMore"]
["Male","TooFast","15OrMore"]
["Male","TooFast","15OrMore"]
["Male","TooFast","15OrMore"]
["Male","TooFast","15OrMore"]
["Male","TooFast","15OrMore"]
["Male","TooFast","15OrMore"]
["Male","TooFast","15OrMore"]
["Male","TooFast","15OrMore"]
["Male","TooFast","15OrMore"]
["Male","TooFast","15OrMore"]
["Male","TooFast","15OrMore"]
["Male","TooFast","15OrMore"]
["Male","Fine","15OrMore"]
["Male","Fine","15OrMore"]
["Male","Fine","15OrMore"]
["Male","Fine","15OrMore"]
["Male","Fine","15OrMore"]
["Male","Fine","15OrMore"]
["Male","Fine","15OrMore"]
["Male","Fine","15OrMore"]
["Male","Fine","15OrMore"]
["Male","Fine","15OrMore"]
["Male","Fine","15OrMore"]
["Male","Fine","15OrMore"]
["Male","Fine","15OrMore"]
["Male","Fine","15OrMore"]
["Male","Fine","15OrMore"]
["Male","Fine","15OrMore"]
["Male","Fine","15OrMore"]
["Male","Fine","15OrMore"]
["Male","Fine","15OrMore"]
["Male","Fine","15OrMore"]
["Male","Fine","15OrMore"]
["Male","Fine","15OrMore"]
["Male","Fine","15OrMore"]
["Male","Fine","15OrMore"]
["Male","Fine","15OrMore"]
["Female","TooFast","Under15"]
["Female","Fine","Under15"]
["Female","Fine","Under15"]
["Female","Fine","Under15"]
["Female","Fine","Under15"]
["Female","Fine","Under15"]
["Female","Fine","Under15"]
["Female","Fine","Under15"]
["Female","Fine","Under15"]
["Female","Fine","Under15"]
["Female","TooFast","15OrMore"]
["Female","TooFast","15OrMore"]
["Female","TooFast","15OrMore"]
["Female","TooFast","15OrMore"]
["Female","TooFast","15OrMore"]
["Female","TooFast","15OrMore"]
["Female","TooFast","15OrMore"]
["Female","TooFast","15OrMore"]
["Female","TooFast","15OrMore"]
["Female","TooFast","15OrMore"]
["Female","TooFast","15OrMore"]
["Female","TooFast","15OrMore"]
["Female","TooFast","15OrMore"]
["Female","TooFast","15OrMore"]
["Female","TooFast","15OrMore"]
["Female","TooFast","15OrMore"]
["Female","TooFast","15OrMore"]
["Female","TooFast","15OrMore"]
["Female","TooFast","15OrMore"]
["Female","TooFast","15OrMore"]
["Female","TooFast","15OrMore"]
["Female","TooFast","15OrMore"]
["Female","TooFast","15OrMore"]
["Female","TooFast","15OrMore"]
["Female","TooFast","15OrMore"]
["Female","TooFast","15OrMore"]
["Female","TooFast","15OrMore"]
["Female","TooFast","15OrMore"]
["Female","TooFast","15OrMore"]
["Female","TooFast","15OrMore"]
["Female","TooFast","15OrMore"]
["Female","TooFast","15OrMore"]
["Female","TooFast","15OrMore"]
["Female","TooFast","15OrMore"]
["Female","TooFast","15OrMore"]
["Female","TooFast","15OrMore"]
["Female","TooFast","15OrMore"]
["Female","TooFast","15OrMore"]
["Female","TooFast","15OrMore"]
["Female","Fine","15OrMore"]
["Female","Fine","15OrMore"]
["Female","Fine","15OrMore"]
["Female","Fine","15OrMore"]
["Female","Fine","15OrMore"]
["Female","Fine","15OrMore"]
["Female","Fine","15OrMore"]
["Female","Fine","15OrMore"]
["Female","Fine","15OrMore"]
["Female","Fine","15OrMore"]
["Female","Fine","15OrMore"]
["Female","Fine","15OrMore"]
["Female","Fine","15OrMore"]
["Female","Fine","15OrMore"]
["Female","Fine","15OrMore"]
["Female","Fine","15OrMore"]
["Female","Fine","15OrMore"]
["Female","Fine","15OrMore"]
["Female","Fine","15OrMore"]
["Female","Fine","15OrMore"]
["Female","Fine","15OrMore"]
["Female","Fine","15OrMore"]
["Female","Fine","15OrMore"]
["Female","Fine","15OrMore"]
["Female","Fine","15OrMore"]
["Female","Fine","15OrMore"]
["Female","Fine","15OrMore"]
["Female","Fine","15OrMore"]
["Female","Fine","15OrMore"]
["Female","Fine","15OrMore"]
["Female","Fine","15OrMore"]
["Female","Fine","15OrMore"]
["Female","Fine","15OrMore"]
["Female","Fine","15OrMore"]
["Female","Fine","15OrMore"]
["Female","Fine","15OrMore"]
["Female","Fine","15OrMore"]
["Female","Fine","15OrMore"]
["Female","Fine","15OrMore"]
["Female","Fine","15OrMore"]
["Female","Fine","15OrMore"]
["Female","Fine","15OrMore"]
["Female","Fine","15OrMore"]
["Female","Fine","15OrMore"]
["Female","Fine","15OrMore"]
["Female","Fine","15OrMore"]
["Female","Fine","15OrMore"]
["Female","Fine","15OrMore"]
["Female","Fine","15OrMore"]
["Female","Fine","15OrMore"]
["Female","Fine","15OrMore"]

{"sheet":"(51)","index":14,"n_rows":51,"n_cols":15,"rows_kept":51,"cut":false,"hidden":false,"merged":[]}
["Company","Stockholders' Equity ($000) ","Market Value","Profit ($000)"]
["AGCO",982.1,372.1,60.6,null,"a",null,"Count of Stockholders' Equity ($000) ","Profit ($000)"]
["AMP",2698,12017.6,2,null,null,null,"Stockholders' Equity ($000) ","0-200","200-400","400-600","600-800","800-1000","1000-1200","Grand Total"]
["Apple Computer",1642,4605,309,null,null,null,"0-1200",10,1,null,null,null,null,11]
["Baxter International",2839,21743,315,null,null,null,"1200-2400",4,10,null,null,2,null,16]
["Bergen Brunswick",629.1,2787.5,3.1,null,null,null,"2400-3600",4,3,3,1,1,null,12]
["Best Buy",557.70000000000005,10376.5,94.5,null,null,null,"3600-4800",null,1,null,null,2,2,5]
["Charles Schwab",1429,35340.6,348.5,null,null,null,"4800-6000",null,2,3,1,null,null,6]
["CMS Energy",2216,4546.6000000000004,285,null,null,null,"Grand Total",18,17,6,2,5,2,50]
["Coca-Cola",2438,15704.9,142]
["Colgate-Palmolive",2085.6,26810.2,848.6]
["Comcast",3815.3,26746.1,972.1,null,"b",null,"Count of Stockholders' Equity ($000) ","Profit ($000)"]
["Conagra",2778.9,15169.4,613.20000000000005,null,null,null,"Stockholders' Equity ($000) ","0-200","200-400","400-600","600-800","800-1000","1000-1200","Grand Total"]
["Conseco",5273.6,11725.7,588.1,null,null,null,"0-1200",0.90909090909090906,9.0909090909090912E-2,0,0,0,0,1]
["Delta Air Lines",4023,9773.7000000000007,1001,null,null,null,"1200-2400",0.25,0.625,0,0,0.125,0,1]
["Dollar General",725.8,7193.1,182,null,null,null,"2400-3600",0.33333333333333331,0.25,0.25,8.3333333333333329E-2,8.3333333333333329E-2,0,1]
["Federated",5709,8301.4,662,null,null,null,"3600-4800",0,0.2,0,0,0.4,0.4,1]
["Fortune Brands",4097.5,5639.2,263.10000000000002,null,null,null,"4800-6000",0,0.33333333333333331,0.5,0.16666666666666666,0,0,1]
["GAP",1573.7,39644.9,824.5,null,null,null,"Grand Total",0.36,0.34,0.12,0.04,0.1,0.04,1]
["Hormel Foods",813.3,2710.1,139.30000000000001]
["Ingersoll-Rand",2707.5,8209.2000000000007,509.1,null,"c","Stockholder's equity and profit seem to be related.  As profit goes up, stockholder's equity goes up. The relationship, however, is not very strong."]
["Ingram Micro",1399.3,2555.6,245.2]
["Kimberly-Clark",3887.2,26881.4,1165.8]
["Kmart",5979,8539.2999999999993,568]
["KN Energy",1223.8,1432,60]
["Kohl's",1162.8,11790.5,192.3]
["Lowe's",3136,22635.3,482.4]
["Marketspan",3000,3850.7,51.3]
["Maytag",507.6,4828.7,280.60000000000002]
["Mead",2252,3155.8,119.7]
["Mellon Bank",4521,19251.3,870]
["Nike",3261.6,15824.9,399.6]
["Nordstrom",1316.7,5683.2,206.7]
["Nucor",2072.6,3734.3,263.7]
["OfficeMax",1138.0999999999999,1014.6,42.6]
["Owens & Minor",161.1,299.60000000000002,20.100000000000001]
["Phelps Dodge",2587.4,2797.3,190.9]
["Qualcomm",957.6,5862.1,108.5]
["Republic Industries",5424.2,6190.9,499.5]
["Safeco",5575.8,5655.6,351.9]
["Safeway",3082.1,27700.7,806.7]
["Shaw Industries",797.4,3110.3,20.6]
["Staples",1514,2995.2,280]
["Sunoco",1514,2995.2,280]
["SuperValue",1201.9000000000001,2701.5,230.8]
["Tenneco",2504,5220.2,255]
["Thermo Electron",2248.1,2218.9,181.9]
["Walgreen",2849,30324.7,511]
["Westvaco",2246.4,2225.6,132]
["Whirlpool",2001,3729.4,325]
["Xerox",5544,35603.699999999997,395]

{"sheet":"(52)","index":15,"n_rows":51,"n_cols":15,"rows_kept":51,"cut":false,"hidden":false,"merged":[]}
["Company","Stockholders' Equity ($000) ","Market Value","Profit ($000)"]
["AGCO",982.1,372.1,60.6,null,"a",null,"Count","Profit ($000)"]
["AMP",2698,12017.6,2,null,null,null,"Market Value","0-200","200-400","400-600","600-800","800-1000","1000-1200","Grand Total"]
["Apple Computer",1642,4605,309,null,null,null,"0-8000",14,13,1,null,null,null,28]
["Baxter International",2839,21743,315,null,null,null,"8000-16000",4,1,3,2,null,1,11]
["Bergen Brunswick",629.1,2787.5,3.1,null,null,null,"16000-24000",null,1,1,null,1,null,3]
["Best Buy",557.70000000000005,10376.5,94.5,null,null,null,"24000-32000",null,null,1,null,3,1,5]
["Charles Schwab",1429,35340.6,348.5,null,null,null,"32000-40000",null,2,null,null,1,null,3]
["CMS Energy",2216,4546.6000000000004,285,null,null,null,"Grand Total",18,17,6,2,5,2,50]
["Coca-Cola",2438,15704.9,142]
["Colgate-Palmolive",2085.6,26810.2,848.6]
["Comcast",3815.3,26746.1,972.1,null,"b",null,"Count","Profit ($000)"]
["Conagra",2778.9,15169.4,613.20000000000005,null,null,null,"Market Value","0-200","200-400","400-600","600-800","800-1000","1000-1200","Grand Total"]
["Conseco",5273.6,11725.7,588.1,null,null,null,"0-8000",0.5,0.4642857142857143,3.5714285714285712E-2,0,0,0,1]
["Delta Air Lines",4023,9773.7000000000007,1001,null,null,null,"8000-16000",0.36363636363636365,9.0909090909090912E-2,0.27272727272727271,0.18181818181818182,0,9.0909090909090912E-2,1]
["Dollar General",725.8,7193.1,182,null,null,null,"16000-24000",0,0.33333333333333331,0.33333333333333331,0,0.33333333333333331,0,1]
["Federated",5709,8301.4,662,null,null,null,"24000-32000",0,0,0.2,0,0.6,0.2,1]
["Fortune Brands",4097.5,5639.2,263.10000000000002,null,null,null,"32000-40000",0,0.66666666666666663,0,0,0.33333333333333331,0,1]
["GAP",1573.7,39644.9,824.5,null,null,null,"Grand Total",0.36,0.34,0.12,0.04,0.1,0.04,1]
["Hormel Foods",813.3,2710.1,139.30000000000001]
["Ingersoll-Rand",2707.5,8209.2000000000007,509.1,null,"c",null,"There appears to be a positive relationship between Profit and Market Value.  As profit goes up, Market Value goes up."]
["Ingram Micro",1399.3,2555.6,245.2]
["Kimberly-Clark",3887.2,26881.4,1165.8]
["Kmart",5979,8539.2999999999993,568]
["KN Energy",1223.8,1432,60]
["Kohl's",1162.8,11790.5,192.3]
["Lowe's",3136,22635.3,482.4]
["Marketspan",3000,3850.7,51.3]
["Maytag",507.6,4828.7,280.60000000000002]
["Mead",2252,3155.8,119.7]
["Mellon Bank",4521,19251.3,870]
["Nike",3261.6,15824.9,399.6]
["Nordstrom",1316.7,5683.2,206.7]
["Nucor",2072.6,3734.3,263.7]
["OfficeMax",1138.0999999999999,1014.6,42.6]
["Owens & Minor",161.1,299.60000000000002,20.100000000000001]
["Phelps Dodge",2587.4,2797.3,190.9]
["Qualcomm",957.6,5862.1,108.5]
["Republic Industries",5424.2,6190.9,499.5]
["Safeco",5575.8,5655.6,351.9]
["Safeway",3082.1,27700.7,806.7]
["Shaw Industries",797.4,3110.3,20.6]
["Staples",1514,2995.2,280]
["Sunoco",1514,2995.2,280]
["SuperValue",1201.9000000000001,2701.5,230.8]
["Tenneco",2504,5220.2,255]
["Thermo Electron",2248.1,2218.9,181.9]
["Walgreen",2849,30324.7,511]
["Westvaco",2246.4,2225.6,132]
["Whirlpool",2001,3729.4,325]
["Xerox",5544,35603.699999999997,395]

{"sheet":"(53)","index":16,"n_rows":51,"n_cols":8,"rows_kept":51,"cut":false,"hidden":false,"merged":[]}
["Profit ($000)","Equity ($000) ","Company","Market Value"]
[60.6,982.1,"AGCO",372.1,null,"a"]
[2,2698,"AMP",12017.6]
[309,1642,"Apple Computer",4605]
[315,2839,"Baxter International",21743]
[3.1,629.1,"Bergen Brunswick",2787.5]
[94.5,557.70000000000005,"Best Buy",10376.5]
[348.5,1429,"Charles Schwab",35340.6]
[285,2216,"CMS Energy",4546.6000000000004]
[142,2438,"Coca-Cola",15704.9]
[848.6,2085.6,"Colgate-Palmolive",26810.2]
[972.1,3815.3,"Comcast",26746.1]
[613.20000000000005,2778.9,"Conagra",15169.4]
[588.1,5273.6,"Conseco",11725.7]
[1001,4023,"Delta Air Lines",9773.7000000000007]
[182,725.8,"Dollar General",7193.1]
[662,5709,"Federated",8301.4]
[263.10000000000002,4097.5,"Fortune Brands",5639.2]
[824.5,1573.7,"GAP",39644.9]
[139.30000000000001,813.3,"Hormel Foods",2710.1]
[509.1,2707.5,"Ingersoll-Rand",8209.2000000000007]
[245.2,1399.3,"Ingram Micro",2555.6,null,"b",null,"There appears to be a direct or positive relationship. As profits go up, Equity goes up."]
[1165.8,3887.2,"Kimberly-Clark",26881.4]
[568,5979,"Kmart",8539.2999999999993]
[60,1223.8,"KN Energy",1432]
[192.3,1162.8,"Kohl's",11790.5]
[482.4,3136,"Lowe's",22635.3]
[51.3,3000,"Marketspan",3850.7]
[280.60000000000002,507.6,"Maytag",4828.7]
[119.7,2252,"Mead",3155.8]
[870,4521,"Mellon Bank",19251.3]
[399.6,3261.6,"Nike",15824.9]
[206.7,1316.7,"Nordstrom",5683.2]
[263.7,2072.6,"Nucor",3734.3]
[42.6,1138.0999999999999,"OfficeMax",1014.6]
[20.100000000000001,161.1,"Owens & Minor",299.60000000000002]
[190.9,2587.4,"Phelps Dodge",2797.3]
[108.5,957.6,"Qualcomm",5862.1]
[499.5,5424.2,"Republic Industries",6190.9]
[351.9,5575.8,"Safeco",5655.6]
[806.7,3082.1,"Safeway",27700.7]
[20.6,797.4,"Shaw Industries",3110.3]
[280,1514,"Staples",2995.2]
[280,1514,"Sunoco",2995.2]
[230.8,1201.9000000000001,"SuperValue",2701.5]
[255,2504,"Tenneco",5220.2]
[181.9,2248.1,"Thermo Electron",2218.9]
[511,2849,"Walgreen",30324.7]
[132,2246.4,"Westvaco",2225.6]
[325,2001,"Whirlpool",3729.4]
[395,5544,"Xerox",35603.699999999997]

{"sheet":"(54)","index":17,"n_rows":51,"n_cols":8,"rows_kept":51,"cut":false,"hidden":false,"merged":[]}
["Profit ($000)","Equity ($000) ","Company","Market Value"]
[60.6,982.1,"AGCO",372.1,null,"a"]
[2,2698,"AMP",12017.6]
[309,1642,"Apple Computer",4605]
[315,2839,"Baxter International",21743]
[3.1,629.1,"Bergen Brunswick",2787.5]
[94.5,557.70000000000005,"Best Buy",10376.5]
[348.5,1429,"Charles Schwab",35340.6]
[285,2216,"CMS Energy",4546.6000000000004]
[142,2438,"Coca-Cola",15704.9]
[848.6,2085.6,"Colgate-Palmolive",26810.2]
[972.1,3815.3,"Comcast",26746.1]
[613.20000000000005,2778.9,"Conagra",15169.4]
[588.1,5273.6,"Conseco",11725.7]
[1001,4023,"Delta Air Lines",9773.7000000000007]
[182,725.8,"Dollar General",7193.1]
[662,5709,"Federated",8301.4]
[263.10000000000002,4097.5,"Fortune Brands",5639.2]
[824.5,1573.7,"GAP",39644.9]
[139.30000000000001,813.3,"Hormel Foods",2710.1]
[509.1,2707.5,"Ingersoll-Rand",8209.2000000000007]
[245.2,1399.3,"Ingram Micro",2555.6,null,"b",null,"There appears to be a slight direct or positive relationship. As MV  go up, Equity goes up."]
[1165.8,3887.2,"Kimberly-Clark",26881.4]
[568,5979,"Kmart",8539.2999999999993]
[60,1223.8,"KN Energy",1432]
[192.3,1162.8,"Kohl's",11790.5]
[482.4,3136,"Lowe's",22635.3]
[51.3,3000,"Marketspan",3850.7]
[280.60000000000002,507.6,"Maytag",4828.7]
[119.7,2252,"Mead",3155.8]
[870,4521,"Mellon Bank",19251.3]
[399.6,3261.6,"Nike",15824.9]
[206.7,1316.7,"Nordstrom",5683.2]
[263.7,2072.6,"Nucor",3734.3]
[42.6,1138.0999999999999,"OfficeMax",1014.6]
[20.100000000000001,161.1,"Owens & Minor",299.60000000000002]
[190.9,2587.4,"Phelps Dodge",2797.3]
[108.5,957.6,"Qualcomm",5862.1]
[499.5,5424.2,"Republic Industries",6190.9]
[351.9,5575.8,"Safeco",5655.6]
[806.7,3082.1,"Safeway",27700.7]
[20.6,797.4,"Shaw Industries",3110.3]
[280,1514,"Staples",2995.2]
[280,1514,"Sunoco",2995.2]
[230.8,1201.9000000000001,"SuperValue",2701.5]
[255,2504,"Tenneco",5220.2]
[181.9,2248.1,"Thermo Electron",2218.9]
[511,2849,"Walgreen",30324.7]
[132,2246.4,"Westvaco",2225.6]
[325,2001,"Whirlpool",3729.4]
[395,5544,"Xerox",35603.699999999997]

{"sheet":"Case01(1)","index":18,"n_rows":105,"n_cols":13,"rows_kept":105,"cut":false,"hidden":false,"merged":[]}
["Use sample to learn about customer base"]
["Evaluate coupon issuance"]
[]
[]
["Customer","Type of Customer (P = Used Coupon; R = Did Not Use Coupon)","Items Purchased","Net Sales (Credit Card Sales","Method of Payment (PC = In Store Credit Card)","Gender","Marital Status","Age",null,"Coupon ?","Frequency"]
[1,"Regular",1,39.5,"Discover","Male","Married",32,null,"Promotional",0.7]
[2,"Promotional",1,102.4,"Proprietary Card","Female","Married",36,null,"Regular",0.3]
[3,"Regular",1,22.5,"Proprietary Card","Female","Married",32,null,"Grand Total",1]
[4,"Promotional",5,100.4,"Proprietary Card","Female","Married",28]
[5,"Regular",2,54,"MasterCard","Female","Married",34,null,"Credit Card","Frequency"]
[6,"Regular",1,44.5,"MasterCard","Female","Married",44,null,"American Express",0.02]
[7,"Promotional",2,78,"Proprietary Card","Female","Married",30,null,"Discover",0.04]
[8,"Regular",1,22.5,"Visa","Female","Married",40,null,"MasterCard",0.14000000000000001]
[9,"Promotional",2,56.52,"Proprietary Card","Female","Married",46,null,"Proprietary Card",0.7]
[10,"Regular",1,44.5,"Proprietary Card","Female","Married",36,null,"Visa",0.1]
[11,"Regular",1,29.5,"Proprietary Card","Female","Married",48,null,"Grand Total",1]
[12,"Promotional",1,31.6,"Proprietary Card","Female","Married",40]
[13,"Promotional",9,160.4,"Visa","Female","Married",40,null,"Gender","Frequency"]
[14,"Promotional",2,64.5,"Visa","Female","Married",46,null,"Female",0.93]
[15,"Regular",1,49.5,"Visa","Male","Single",24,null,"Male",7.0000000000000007E-2]
[16,"Promotional",2,71.400000000000006,"Proprietary Card","Male","Single",36,null,"Grand Total",1]
[17,"Promotional",3,94,"Proprietary Card","Female","Single",22]
[18,"Regular",3,54.5,"Discover","Female","Married",40,null,"Marital Status","Count of Marital Status"]
[19,"Promotional",2,38.5,"MasterCard","Female","Married",32,null,"Married",0.84]
[20,"Promotional",6,44.8,"Proprietary Card","Female","Married",56,null,"Single",0.16]
[21,"Promotional",1,31.6,"Proprietary Card","Female","Single",28,null,"Grand Total",1]
[22,"Promotional",4,70.819999999999993,"Proprietary Card","Female","Married",38]
[23,"Promotional",7,266,"American Express","Female","Married",50,null,"# Items Purchased","Frequency"]
[24,"Regular",2,74,"Proprietary Card","Female","Married",42,null,1,0.28999999999999998]
[25,"Promotional",2,39.5,"Visa","Male","Married",48,null,2,0.27]
[26,"Promotional",1,30.02,"Proprietary Card","Female","Married",60,null,3,0.1]
[27,"Regular",1,44.5,"Proprietary Card","Female","Married",54,null,4,0.1]
[28,"Promotional",5,192.8,"Proprietary Card","Female","Single",42,null,5,0.09]
[29,"Promotional",3,71.2,"Proprietary Card","Female","Married",32,null,6,7.0000000000000007E-2]
[30,"Promotional",1,18,"Proprietary Card","Female","Married",70,null,"7 or more",0.08]
[31,"Promotional",2,63.2,"MasterCard","Female","Married",28,null,"Grand Total",1]
[32,"Regular",1,75,"Proprietary Card","Female","Married",52]
[33,"Promotional",3,63.2,"Proprietary Card","Female","Married",44,null,"Net Sales","Frequency"]
[34,"Regular",1,40,"Proprietary Card","Female","Married",34,null,"10 up to 60",0.51]
[35,"Promotional",5,105.5,"MasterCard","Female","Married",56,null,"60 up to 110",0.28999999999999998]
[36,"Regular",1,29.5,"MasterCard","Male","Single",36,null,"110 up to 160",0.12]
[37,"Regular",2,102.5,"Visa","Female","Single",42,null,"160 up to 210",0.04]
[38,"Promotional",6,117.5,"Proprietary Card","Female","Married",50,null,"210 up to 260",0.02]
[39,"Promotional",5,13.23,"Proprietary Card","Female","Married",44,null,"260 up to 310",0.02]
[40,"Regular",2,52.5,"Proprietary Card","Female","Married",58,null,"Grand Total",1]
[41,"Promotional",13,198.8,"Proprietary Card","Female","Married",42]
[42,"Promotional",4,19.5,"Visa","Female","Married",46,null,"Count","Coupon"]
[43,"Regular",2,123.5,"Proprietary Card","Female","Married",48,null,"Credit Card","Promotional","Regular","Grand Total"]
[44,"Promotional",1,62.4,"Proprietary Card","Female","Married",54,null,"American Express",1,1,2]
[45,"Promotional",2,23.8,"Proprietary Card","Female","Married",38,null,"Discover",null,4,4]
[46,"Promotional",2,39.6,"Proprietary Card","Female","Married",60,null,"MasterCard",7,7,14]
[47,"Regular",1,25,"MasterCard","Female","Married",46,null,"Proprietary Card",57,13,70]
[48,"Promotional",3,63.64,"Proprietary Card","Female","Married",30,null,"Visa",5,5,10]
[49,"Promotional",1,14.82,"Proprietary Card","Female","Married",32,null,"Grand Total",70,30,100]
[50,"Promotional",9,145.19999999999999,"MasterCard","Female","Married",46]
[51,"Promotional",6,176.62,"Proprietary Card","Female","Married",38,null,"Frequency","Coupon"]
[52,"Promotional",5,118.8,"Proprietary Card","Male","Married",68,null,"Net Sales","Promotional","Regular","Grand Total"]
[53,"Regular",1,58,"Discover","Female","Single",78,null,"10 up to 60",32,19,51]
[54,"Regular",2,74,"Visa","Female","Single",20,null,"60 up to 110",21,8,29]
[55,"Regular",2,49.5,"MasterCard","Female","Married",32,null,"110 up to 160",9,3,12]
[56,"Promotional",3,141.6,"Proprietary Card","Female","Married",38,null,"160 up to 210",4,null,4]
[57,"Promotional",6,123.1,"Proprietary Card","Female","Married",54,null,"210 up to 260",2,null,2]
[58,"Promotional",2,80.400000000000006,"Proprietary Card","Female","Married",48,null,"260 up to 310",2,null,2]
[59,"Promotional",4,65.2,"MasterCard","Female","Married",46,null,"Grand Total",70,30,100]
[60,"Promotional",4,113,"Proprietary Card","Female","Single",50]
[61,"Promotional",1,108.8,"Proprietary Card","Female","Married",46,null,"Age","Count of Age"]
[62,"Promotional",3,59.91,"Proprietary Card","Female","Single",30,null,"20-29",10]
[63,"Promotional",5,53.6,"Proprietary Card","Female","Married",54,null,"30-39",30]
[64,"Promotional",1,31.6,"Proprietary Card","Female","Single",42,null,"40-49",33]
[65,"Promotional",2,49.5,"Proprietary Card","Female","Married",48,null,"50-59",16]
[66,"Promotional",1,39.6,"Proprietary Card","Female","Married",62,null,"60-69",7]
[67,"Promotional",2,59.5,"Proprietary Card","Female","Married",34,null,"70-80",4]
[68,"Promotional",5,146.80000000000001,"Proprietary Card","Female","Married",28,null,"Grand Total",100]
[69,"Promotional",2,47.2,"Proprietary Card","Male","Married",46]
[70,"Promotional",8,95.05,"Proprietary Card","Female","Married",54,null,"Sum","Coupon"]
[71,"Promotional",5,155.32,"Proprietary Card","Female","Married",30,null,"Net Sales","Promotional","Regular","Grand Total"]
[72,"Promotional",4,58,"MasterCard","Female","Married",32,null,"10 up to 60",1258.28,776,2034.28]
[73,"Regular",1,69,"Proprietary Card","Female","Single",22,null,"60 up to 110",1704.8500000000001,656.5,2361.3500000000004]
[74,"Promotional",2,46.5,"Proprietary Card","Female","Married",32,null,"110 up to 160",1172.46,427.25,1599.71]
[75,"Promotional",2,45.22,"Proprietary Card","Female","Married",74,null,"160 up to 210",728.62,null,728.62]
[76,"Promotional",4,84.74,"Proprietary Card","Female","Married",62,null,"210 up to 260",482.5,null,482.5]
[77,"Regular",2,39,"Proprietary Card","Female","Married",42,null,"260 up to 310",553.58999999999992,null,553.58999999999992]
[78,"Promotional",4,111.14,"Proprietary Card","Female","Married",28,null,"Grand Total",5900.3,1859.75,7760.05]
[79,"Promotional",3,86.8,"Proprietary Card","Female","Married",38]
[80,"Regular",2,89,"Discover","Female","Married",54]
[81,"Promotional",2,78,"MasterCard","Female","Married",68]
[82,"Promotional",6,53.2,"Proprietary Card","Female","Single",30]
[83,"Promotional",4,58.5,"Visa","Female","Married",36]
[84,"Promotional",3,46,"Proprietary Card","Female","Married",44]
[85,"Regular",2,37.5,"Visa","Female","Married",44]
[86,"Promotional",1,20.8,"Proprietary Card","Female","Married",62]
[87,"Regular",6,144,"MasterCard","Female","Single",48]
[88,"Regular",4,107,"Proprietary Card","Female","Married",36]
[89,"Promotional",1,31.6,"Proprietary Card","Female","Single",20]
[90,"Promotional",6,57.6,"Proprietary Card","Female","Married",42]
[91,"Promotional",4,95.2,"Proprietary Card","Female","Married",54]
[92,"Promotional",1,22.42,"Proprietary Card","Female","Married",54]
[93,"Regular",5,159.75,"Proprietary Card","Female","Married",72]
[94,"Promotional",17,229.5,"Proprietary Card","Female","Married",30]
[95,"Regular",3,66,"American Express","Female","Married",46]
[96,"Regular",1,39.5,"MasterCard","Female","Married",44]
[97,"Promotional",9,253,"Proprietary Card","Female","Married",30]
[98,"Promotional",10,287.58999999999997,"Proprietary Card","Female","Married",52]
[99,"Promotional",2,47.6,"Proprietary Card","Female","Married",30]
[100,"Promotional",1,28.44,"Proprietary Card","Female","Married",44]

{"sheet":"Case01(2)","index":19,"n_rows":7,"n_cols":2,"rows_kept":7,"cut":false,"hidden":false,"merged":[]}
[1,"70% of the sales over this period were from promotions. So the promotion seems to be popular"]
[2,"70 % of the credit card sales used the proprietary card. This is surprising because they said that they did not send the coupon to regular customers. Since regular customers are more likely to have the proprietary card and the new first time customers would not have a proprietary card, this is unusual. Did the new customers using the promotion sign up for proprietary cards on their first visit?"]
[3,"93% of the sales were to women. 84% of the sales were to married people."]
[4,"Over 50% (29% + 27%) of the sales were made for 2 or fewer items purchased. Similarly, 80% of the net sales amounts were for $110 or less. Also, 53 of the 70 promotional sales were for $110 or less."]
[5,"57 of the 70 Promotional sales were using the proprietary card. As stated above, this seems unusual."]
[6,"63% of the sales were to people between the ages of 30 and 49. The demographic seems to be: female, married, and 30 - 49 years old."]
[7,"The number of sales and the sum of sales for the promotional customers is greater than for the regular customers. Here again, it seems that the promotion has the net effect of increased sales."]