# RE: st: Dependent variable is a proportion

 From "Nick Cox" To Subject RE: st: Dependent variable is a proportion Date Thu, 13 May 2004 12:24:16 +0100

```* top line to be sacrificed?

Another case of "Statalist ate my top line" !

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

> -----Original Message-----
> From: owner-statalist@hsphsun2.harvard.edu
> [mailto:owner-statalist@hsphsun2.harvard.edu]On Behalf Of Nick Cox
> Sent: 13 May 2004 11:58
> To: statalist@hsphsun2.harvard.edu
> Subject: RE: st: Dependent variable is a proportion
>

Joe: Would you please spell out your objections to the
> 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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