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From | Austin Nichols <austinnichols@gmail.com> |
To | statalist@hsphsun2.harvard.edu |
Subject | Re: st: Modeling % data |
Date | Wed, 22 Sep 2010 11:17:56 -0400 |
Marlis Gonzalez Fernandez <mgonzal5@jhmi.edu>: Quantile regression via -qreg- makes sense, though then you are modeling how conditional quantiles change as a fn of predictors, not the conditional mean. If you want to model the conditional mean, consider a GLM (-help glm-), which offers one way to model fractional outcomes via the fractional logit; see e.g. http://www.stata.com/support/faqs/stat/logit.html http://www.stata.com/meeting/12uk/Buis_proportions.pdf http://cohesion.rice.edu/Conferences/Econometrics/emplibrary/wooldridge.pdf http://www.stata.com/meeting/snasug08/abstracts.html#wooldridge and see also -locpr- on SSC for an alternative conditional mean model as a fn of one predictor. On Wed, Sep 22, 2010 at 11:03 AM, Marlis Gonzalez Fernandez <mgonzal5@jhmi.edu> wrote: > My outcome variable is a % (% error in a language test). We do have many 0 and 100. I need to be able to do a multiple regression to adjust for known predictors of the variable vs. the predictors of interest. > > It was suggested that I use qreg. I've done so and it seems to work. Any thoughts? I can provide more details if necessary. * * 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/