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RE: st: Dependent variable is a proportion


From   "Nick Cox" <n.j.cox@durham.ac.uk>
To   <statalist@hsphsun2.harvard.edu>
Subject   RE: st: Dependent variable is a proportion
Date   Thu, 13 May 2004 11:58:18 +0100

Gaussian family in this context? 

Nick 
n.j.cox@durham.ac.uk 

Jhilbe@aol.com
 
> If you have the denominator information you can use either a 
> grouped logistic or a poisson regression with offset. For 
> instance, suppose that the proportion of 16 years olds taking 
> a certain course is .30, meaning 30 students took the course 
> at a particular site which had a total of 100 students who 
> could have possibly taken the course. Of course, the same 
> proportion would obtain if there were 45 students taking the 
> course out of a possible 150. In fact, if you know the 
> proportion AND the denominator you can calculate the numerator
> and you've got all you need for a rate parameterization 
> Poisson regression model (if the proportions are generally 
> small), or grouped logistic regression (if the proportions 
> are relatively large). 
> 
> Someone else may have a better idea on this, but this is my 
> thought on it.  You do not want to use a logit link with a 
> Gauassian family.   
 
> > The dependent variable I have is a proportion (percentage of 16 year
> > olds enrolled in a particular subject) which is between 0 and 86
> > percent. I am not sure about the linear form. My dependent 
> variable is 0
> > only in 3,980 cases out of 112,412 sample obs. Here a zero is a
> > structural one, because the school does not offer history (which is
> > choice subject). 
> > 
> > Would somebody suggest to me whether it would be better to perform a
> > logit transformation, or estimate -glm- with 
> > family(gaussian) and
> > link(logit). Any suggestion would be greatly appreciated!
 

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