For the data listed below, USE EXCEL (SOLVER if necessary) to fit a logistic regression model to predict purchase decisions. Identify significant predictors and comment on the classification accuracy.

Code definitions: Purchase 0 – Not purchased and 1 – Purchased; Gender 1 – Male and 2 – Female
 

Transcribed Image Text:

Income ($ Purchase '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 1 122.8 43 0 121.8 45 1 126.6 41 1 125.8 46 1 138.8 42 222222 0 149.6 37 1 1 159.5 33 2 Income ($ Purchase '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 1 122.8 43 0 121.8 45 1 126.6 41 1 125.8 46 1 138.8 42 222222 0 149.6 37 1 1 159.5 33 2