Logistic Regression Prediction of p | ||||||

X-Variables | Coefficients | X-values | Product | |||

Intercept | -17.910000 | 1 | -17.91 | <------ | Use the "trick" of | |

Income | 0.000202 | 58000 | 11.6928 | multiplying the | ||

Is Female | 1.646000 | 1 | 1.646 | intercept by 1 just | ||

Is Married | 0.566200 | 1 | 0.5662 | to carry it over to | ||

Has College | -0.279400 | 1 | -0.2794 | the product column | ||

Is Professional | 0.225300 | 0 | 0 | |||

Is Retired | -1.159000 | 0 | 0 | |||

Unemployed | 0.988600 | 0 | 0 | |||

Residence Length | 0.024680 | 8 | 0.19744 | |||

Dual Income | 0.451800 | 1 | 0.4518 | |||

Minors | 1.133000 | 0 | 0 | |||

Own | 1.056000 | 1 | 1.056 | |||

House | -0.926500 | 1 | -0.9265 | |||

White | 1.864000 | 1 | 1.864 | |||

English | 1.530000 | 1 | 1.53 | |||

Prev Child Mag | 1.557000 | 0 | 0 | |||

Prev Parent Mag | 0.477700 | 1 | 0.4777 | |||

0.37 | <------- | log(p/(1-p)) = sum of | ||||

products in column D | ||||||

p = | 0.591 | <------- | formula is: | |||

=exp(F22)/(1+exp(F22)) |