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Re: st: Modeling % data


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.

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