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%.