# How to cope with multicollinearity and interactions between IVs in generalized linear models?

I have made a generalised linear model with a single response variable (continuous/normally distributed) and 4 explanatory variables (3 of which are factors and the fourth is an integer). I have used a Gaussian error distribution with an identity link function.

Do I need to check for multicollinearity and interactions amongst explanatory variables? If yes, how do I do this with categorical explanatory variables?

• If you plan on interpreting the individual $p$-values, you should check for collinearity. Checking for collinearity is a frequently discussed topic on this website so doing a search for 'collinearity' will probably be fruitful. To look at dependence between categorical variables you can look at the usual $\chi^2$ tests or something like that. In this thread,stats.stackexchange.com/questions/8088/…, the problem is discussed for binary predictors – Macro Jul 15 '12 at 13:23