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From | Eduardo Nunez <enunezb@gmail.com> |
To | statalist@hsphsun2.harvard.edu |
Subject | Re: st: variable transformation and centering |
Date | Sun, 3 Apr 2011 20:46:58 -0400 |
Thank you Nick. Eduardo On Sun, Apr 3, 2011 at 8:18 PM, Nick Cox <njcoxstata@gmail.com> wrote: > The marginal distribution of any predictor does not have implications > as such for regression. > > Possibly, transforming skewed predictors may indirectly improve > matters in so far as assumptions of linearity, additivity and equal > variances are more nearly satisfied. > > However, I suggest that your question answers itself if you focus on > one detail. From what you say, transformations such as logarithm or > square root are the most likely candidates. Translating your > predictors first by subtracting a summary will produce variables with > positive and negative values which will only be more difficult to > transform. So, don't centre first. Typically, centring will not be > necessary after transformation either. > > On Sun, Apr 3, 2011 at 10:08 PM, Eduardo Nunez <enunezb@gmail.com> wrote: > >> I have to run a regression model with several inflammatory biomarkers >> as dependent variables. They are continuous and heavily skewed >> variables. >> I would like to transform and center them before including in the model. >> My question is: should I transform the variable first and then center >> it? Or is preferred to center the variable first and then to transform >> it? >> Or any way doesn't make difference? > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ > * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/