I am running simple linear models(Y~X) in R where my predictor is a categorical variable (0-10). However, this variable is not normally distributed and none of the transformation techniques available are healpful (e.g. log, sq etc.) as the data is not negatively/positively skewed but rather all over the place. I am aware that for lm the outcome variable (Y) has to be normally distributed but is this also required for predictors? If yes, any suggestions of how to do this would be more than welcome.
Also, as the data I am looking at has two groups, patients vs controls (I am interested in group differences, as you can guess), do I have to look at whether the data is normally distributed within the two groups or overall across the two groups?
Thanks.
See @Roman Luštriks comment above: it does not matter how your predictors are distributed. (Except for problems with multicollinearity.) What is important is that the residuals be normal (and with homogeneous variances).