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


From   Nick Cox <njcoxstata@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 16:53:27 +0000

I agreed. If you want to talk about quantile regression, then the
focus changes.

Nick

On Mon, Dec 17, 2012 at 4:45 PM, JVerkuilen (Gmail)
<jvverkuilen@gmail.com> wrote:
> 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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