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st: Dependent var is a proportion, with large spike in .95+


From   "Dan Weitzenfeld" <dan.weitzenfeld@emsense.com>
To   statalist@hsphsun2.harvard.edu
Subject   st: Dependent var is a proportion, with large spike in .95+
Date   Wed, 3 Sep 2008 13:22:14 -0700

Hi Statalist,
I am trying to determine which testing factors drive a proportion
dependent variable, PercentNoise.
In searching the archives, I came across -betafit-, and a link to the
FAQ: "How do you fit a model when the dependent variable is a
proportion?"  In that response, Allen McDowell and Nic Cox write, "In
practice, it is often helpful to look at the frequency distribution: a
marked spike at zero or one may well raise doubt about a single model
fitted to all data."
That describes my situation exactly:  I have a marked spike in my
histogram at the top bin, roughly .95 - 1.  I am wondering how to
account for this.
Does -betafit- take such a possibility into account?
Can someone briefly describe how I could use multiple models to fit
all the data, as implied in the FAQ response?
My fallback is setting a pass/fail bar and converting my proportions
to a binary, then using probit/logit.  But the obvious drawback is
that I am throwing away information by collapsing the continuous
(albeit bounded) proportion variable to a binary.

Thanks in advance for any suggestions,
Dan
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