When I build my most parsimonious model using a backward selection approach, do I have to worry about multicollinearity. I mean, do I first check for multicollnearity and drop the variables which has highly collinear and then put only those independent variables in my model that are not correlated and then run a backward selection approach to build the most parsimonious model.
Or should I just put all variables in the model irrespective of whether there is multicollnearity issue since anyway I will end up with the most parsimonious model using a backward selection appraoch.
mdl<-lm(y ~ x1 + x2 + x3 + x4 + x5 + x6 + x7) drop1(mdl, test="F")
Please let me know if the question is not clear and I will try to change it.