For the data listed below, PurchaseIncome ($ ‘000)AgeGender071.94220100.44210105.6441183.13920114.24311113.54410115.24210100.4352092.64320123.84210122
For the data listed below,
|
Purchase |
Income ($ ‘000) |
Age |
Gender |
|
0 |
71.9 |
42 |
2 |
|
0 |
100.4 |
42 |
1 |
|
0 |
105.6 |
44 |
1 |
|
1 |
83.1 |
39 |
2 |
|
0 |
114.2 |
43 |
1 |
|
1 |
113.5 |
44 |
1 |
|
0 |
115.2 |
42 |
1 |
|
0 |
100.4 |
35 |
2 |
|
0 |
92.6 |
43 |
2 |
|
0 |
123.8 |
42 |
1 |
|
0 |
122.8 |
45 |
1 |
|
1 |
98.6 |
46 |
2 |
|
0 |
107.6 |
41 |
2 |
|
0 |
108.4 |
42 |
2 |
|
1 |
138.8 |
41 |
1 |
|
1 |
109.9 |
44 |
2 |
|
1 |
136.2 |
47 |
1 |
|
1 |
117.6 |
38 |
2 |
|
1 |
122.8 |
43 |
2 |
|
0 |
121.8 |
45 |
2 |
|
1 |
126.6 |
41 |
2 |
|
1 |
125.8 |
46 |
2 |
|
1 |
138.8 |
42 |
2 |
|
0 |
149.6 |
37 |
1 |
|
1 |
159.5 |
33 |
2 |
Code definitions: Purchase 0 – Not purchased and 1 – Purchased; Gender 1 – Male and 2 – Female
Fit a logistic regression model to predict purchase decision. Identify significant predictors and comment on classification accuracy.