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


From   David Hoaglin <dchoaglin@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: OLS assumptions not met: transformation, gls, or glm as solutions?
Date   Thu, 20 Dec 2012 23:21:14 -0500

When you run -regress-, the interpretation is not quite that simple.
It needs to mention the adjustments for the contributions of the other
variables in the model.

The same qualification applies in the logistic regression model.

David Hoaglin

On Thu, Dec 20, 2012 at 7:33 PM, Alan Acock <acock@me.com> wrote:
> If I run
>
> regress qual_p conf_p i.sexrare ston_p forg_p sacr_p
>
> were all variables but for sexrare are proportion of the maximum possible value, the interpretations are simple. A change in conf_p of one percentage point predicts a xx(coefficient) percentage point change in the outcome.
>
> When I run
>
> glm qual_p conf_p i.sexrare ston_p forg_p sacr_p, ///
>  family(binomial) link(logit) vce(robust)
>
> is there a clear interpretation of the coefficient or some transformation of the coefficients?
>
> I'm think the answer should be obvious to me, but it is not.
>
> Alan Acock
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