This is probably a really basic question, but it's the first time I've created a model that defines Poisson as its error family.
In setting up my variables to make the model, should I be concerned about whether or not predictor variables are normally distributed, and if not, should I be attempting to transform them to make them normal? Or alternatively, should the residuals of simple regressions between each predictor and the response variable be normally distributed? Or is this something I look at overall, once the model is made, by looking at a histogram of the residuals of the full model? Or, does the normal distribution not even apply in this case because I have specified Poisson errors?
Best Answer
(1) No regression, Poisson or otherwise, makes any assumption about the distribution of the predictors.
(2) You should check how the residuals vary against the fitted values but they are only asymptotically normal.