I realize that the following has been talked about on this list many times before in some related way but I am going to ask for help anyway because I still don't know what to do.
Suppose I have no intercept models such as the following : Y = B*X_1 + error Y = B*X_2 + error Y = B*X_3 + error Y = B*X_4 + error and I run regressions on each ( over the same sample of Y ) and now I want to evaluate which X has the greatest predictive power. I'm fairly certain that R squared is not applicable because of the lack of an intercept but I was wondering what was ? Any references to this particular problem or suggestions are appreciated. I honestly believe that including an intercept is incorrect For my particular problem. Thanks. Maybe I could put all the X's in one regression and some kind of topdownselect or StepAIC algorithm for example ? Thanks. -------------------------------------------------------- This is not an offer (or solicitation of an offer) to buy/se...{{dropped}} ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.