In: Statistics and Probability
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.
I have used SPSS to run the logistic regression:
Following are the output
From table 2 we can write logistic regression model as
Here p is probabilty of purchase and given by
Significant predictors are Income and Gender as their sig. values are less than 0.05.
From table 3 , observe that overall classification accuracy is 76%.