Reference no: EM132819394
You're looking to build a model which identifies the type of person that responded to your marketing campaign and purchased a new smartphone. The data describes your database that recorded your activities.
Prev Marital Time Name Carrier Age Residence Purch Income Status Gender Purchased
Jan 1998 BGH T-mob 25 City 3 $55,000 Married M 1
Jan 1998 TYR Verizon 37 Suburb 1 $66,000 Single F 0
Jan 1998 NMM Verizon 55 City 2 $68,000 Single F 1
Jan 1998 EEH Sprint 25 City 2 $35,000 Married F 1
Jan 1998 TSR ATT 30 Suburb 5 $53,000 Mingle M 0
Jan 1998 JER ATT 58 City 1 $98,000 Married M 1
Jan 1998 LLH Sprint 45 City 5 $25,000 Single M 0
Jan 1998 BNR Verizon 47 Suburb 1 $26,000 Married F 0
Jan 1998 MVR ATT 25 Suburb 4 $48,000 Married F 1
Jan 1998 BKR T-mob 22 City 4 $75,000 Married F 1
Jan 1998 GSR Sprint 40 City 3 $90,600 Single F 1
Jan 1998 JKR Sprint 65 City 1 $28,000 Married M 1
Jan 1998 MLH Verizon 56 Suburb 6 $65,000 Married M 1
Jan 1998 ABN ATT 30 City 2 $57,000 Single M 0
Jan 1998 MYR ATT 25 City 1 $68,000 Married F 0
"Time" The time when the marketing campaign was run
"Name" Initials of the person that was sent a marketing mailer.
"Carrier" The carrier the person now has for their phone
"Age" Age of the person who received the mailer
"Residence" Where the person lives
"Prev Purch" The number of phones the person purchased in the past 5 years
"Income" Income of the person
"Marital Stat" Marital Status of the person
"Gender" Gender of the person
"Purchased" The target variable (1 if the person purchased a phone, 0 if no)
Results of your regression analysis
Variable T-Statistic
Carrier 2.272 R^2 = .71
Income -3.120 F Statistic = 35
Residence 1.870 Durbin Watson = 1.85
Prev Purch 2.051
Marital Stat 2.470
Gender 1.920
Age 1.551
Use the information above (the sample data information and results of your regression analysis to answer the following questions). Simply answers in the space following the question (4 pts each)
1) Is this data cross-sectional or time series?
2) Given the T-Statistics, what variables could you rely on in making a business decision (assuming you have enough data)?
3) Does your model have a problem regarding missing any important variables (e.g. does it illustrate autocorrelation)? Yes/No
4) What does the R^2 term describe the model. In other words, if someone asked you what the R^2 the term meant, how would you describe it?
5) What does the negative sign for the t-statistic on the Income variable imply?
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