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Re: st: OLS assumptions not met: transformation, gls, or glm as solutions?


From   "JVerkuilen (Gmail)" <jvverkuilen@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: OLS assumptions not met: transformation, gls, or glm as solutions?
Date   Mon, 17 Dec 2012 11:45:48 -0500

On Mon, Dec 17, 2012 at 11:41 AM, Nick Cox <njcoxstata@gmail.com> wrote:
> We can converge by holding fast to the idea that regression is about
> modelling the conditional mean. If you have a inappropriate model for
> the conditional mean, wondering how well you can fit it is not a very
> interesting or useful question.

Well conditional quantities, most typically the conditional mean, but
of course there's conditional quantiles, conditional variances, etc.,
depending on what you want to know. But 100%, if the model itself is
bad then you are well and truly scrod.

I'm sure all of us in our history as data analysts (broadly speaking)
have committed some real doozies in that regard. I know I have.
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