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Re: st: Modeling % data
From
Austin Nichols <[email protected]>
To
[email protected]
Subject
Re: st: Modeling % data
Date
Wed, 22 Sep 2010 11:17:56 -0400
Marlis Gonzalez Fernandez <[email protected]>:
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
<[email protected]> 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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