Table 2.6; it was selected randomly from a larger database to be the training set.
Personal Loan indicates whether a solicitation for a personal loan was accepted and is the response variable.
A campaign is planned for a similar solicitation in the future, and the bank is looking for a model that will identify likely responders.
Examine the data carefully and indicate what your next step would be.
|
Table 2.6 Sample from a bank database
OBS AGE EXPERIENCE INCOME ZIP
|
CC
FAMILY AVG EDUC MORTGAGE
|
PERSONAL SECURITIES
WAN ACCT
|
|
1
|
25
|
1
|
49 91107
|
4
|
1.6
|
1
|
0
|
0 1
|
|
4
|
35
|
9
|
100 94112
|
1
|
2.7
|
2
|
0
|
0 0
|
|
5
|
35
|
8
|
45 91330
|
4
|
1.0
|
2
|
0
|
0 0
|
|
9
|
35
|
10
|
81 90089
|
3
|
0.6
|
2
|
104
|
0 0
|
|
10
|
34
|
9
|
180 93023
|
1
|
8.9
|
3
|
0
|
1 0
|
|
12
|
29
|
5
|
45 90277
|
3
|
0.1
|
2
|
0
|
0 0
|
|
17
|
38
|
14
|
130 95010
|
4
|
4.7
|
3
|
134
|
1 0
|
|
18
|
42
|
18
|
81 94305
|
4
|
2.4
|
1
|
0
|
0 0
|
|
21
|
56
|
31
|
25 94015
|
4
|
0.9
|
2
|
III
|
0 0
|
|
26
|
43
|
19
|
29 94305
|
3
|
0.5
|
1
|
97
|
0 0
|
|
29
|
56
|
30
|
48 94539
|
1
|
2.2
|
3
|
0
|
0 0
|
|
30
|
38
|
13
|
119 94104
|
1
|
3.3
|
2
|
0
|
1 0
|
|
35
|
31
|
5
|
50 94035
|
4
|
1.8
|
3
|
0
|
0 0
|
|
36
|
48
|
24
|
81 92647
|
3
|
0.7
|
1
|
0
|
0 0
|
|
37
|
59
|
35
|
121 94720
|
1
|
2.9
|
1
|
0
|
0 0
|
|
38
|
51
|
25
|
71 95814
|
1
|
1.4
|
3
|
198
|
0 0
|
|
39
|
42
|
18
|
141 94114
|
3
|
5.0
|
3
|
0
|
1 1
|
|
41
|
57
|
32
|
84 92672
|
3
|
1.6
|
3
|
0
|
0 1
|
In fitting a model to classify prospects as purchasers or nonpurchasers, a certain company drew the training data from internal data that include demographic and purchase information.
Future data to be classified will be lists purchased from other sources, with demographic (but not purchase) data included.
It was found that "refund issued" was a useful predictor in the training data. Why is this not an appropriate variable to include in the model?