I am working on a credit scoring modelling project and we decided to use dummy variables for regression. The way we create dummy variables are:
For each predicting numeric variable,
We create by default 10 equal-size bins, examine the weights of evidence (WOE) for each bin and merge adjacent bins if their WOEs are similar (meaning their risk signals are similar.
Then we create dummy variables fomr this numeric variable according to the final bins. Say if we end up with 3 bins for this variable, then we create 3 dummy variables representing the bins. Then we pick one dummy variable as reference to avoid perfect multicollinearity.
Then we run logistic regression.
The above is the highlevel description of how we do the modelling. My question is not specific to this procedure but more of a general dummy variable regression question: In the case when we have many variables (~20) for regression, the number of dummy variables will be even more and the regression result often says some dummy variables are insignificant. How do you treat insignificant coefficient estimates? What if a variable is significant in variable level, having some significant dummy variables but not all?