=?iso-8859-1?Q?Sami_Ullah?= wrote: > Hey Ecologers: > > I have a various variables for running multiple linear regression model > using GLM. Some of my predictor variables are non-normally distributed. > Using multiple linear regression, I use proc-univariate to check if the > residuals in the regression model met the normality criteria, which the > model did. > > Now I am wondering if it is advisable if I can keep the skewed predictor > variables in the model or have to go for non-parametric analysis? > > The distribution of the predictor variables is irrelevant, so you can happily keep them in. Well, the distribution is almost irrelevant. You can get problems if they are co-linear (i.e. highly correlated), or if you have outliers (which can have a large influence on the fit).
I've come across the impression that the predictors have to be normally distributed a few times, but I don't know where it originates from - certainly not from statistical theory. Bob -- Bob O'Hara Department of Mathematics and Statistics P.O. Box 68 (Gustaf Hällströmin katu 2b) FIN-00014 University of Helsinki Finland Telephone: +358-9-191 51479 Mobile: +358 50 599 0540 Fax: +358-9-191 51400 WWW: http://www.RNI.Helsinki.FI/~boh/ Blog: http://deepthoughtsandsilliness.blogspot.com/ Journal of Negative Results - EEB: www.jnr-eeb.org
