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Re: st: OLS assumptions not met: transformation, gls, or glm as solutions? (Out of Office Autoreply: sarah.miller@lshtm.ac.uk)


From   <Sarah.Miller@lshtm.ac.uk>
To   <statalist@hsphsun2.harvard.edu>
Subject   Re: st: OLS assumptions not met: transformation, gls, or glm as solutions? (Out of Office Autoreply: sarah.miller@lshtm.ac.uk)
Date   Fri, 21 Dec 2012 00:36:39 +0000

I am out of the office until Tuesday 8th January 2012.  I shall deal with your enquiry upon my return.  Enjoy the festive season!

Sarah Miller 
Pathways Administrator
------------------------------------------------------
Department of Medical Statistics
London School of Hygiene & Tropical Medicine
Keppel Street
London
WC1E 7HT
http://pathways.lshtm.ac.uk 

>>> <Sarah.Miller@lshtm.ac.uk> 12/21/12 00:35 >>>

I am out of the office until Tuesday 8th January 2012.  I shall deal with your enquiry upon my return.  Enjoy the festive season!

Sarah Miller 
Pathways Administrator
------------------------------------------------------
Department of Medical Statistics
London School of Hygiene & Tropical Medicine
Keppel Street
London
WC1E 7HT
http://pathways.lshtm.ac.uk 

>>> Alan Acock <acock@me.com> 12/21/12 00:33 >>>

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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