# RE: st: Dependent variable is a proportion

 From "Nick Cox" To 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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