I ran a logistic regression model on SPSS with a dependent variable of yes/no whether you chose bus or not (the other being personal vehicle) and 5 independent variables (Waiting Time, Trip Time, Total Daily Expense, Overall Mode Comfort, and Overall Mode Ease-of-use). While the Omnibus and Hosmer-Lemeshow tests shows the model to be very good, and the significance for the most variables adequate, the result coefficients of some of the variables are somewhat off. This affects the probability estimation in that the predictor variable goes against intuition in real life conditions.
For example, the Comfort variable has a coefficient of -0.102821; this translates to a low probability when the Comfort value is high. Who wouldn't choose the bus when the Comfort value is over the top? I'm thinking that the coefficient should be a positive instead of negative. I should probably also point out that the intercept is negative, I'm not sure how much this effects the model.
So what seems to be the problem with my model?