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re: st: QREG Question
"Ariel Linden, DrPH" <email@example.com>
re: st: QREG Question
Sat, 28 Jul 2012 12:41:40 -0400
It seems to me that you have data that fits a Poisson-like distribution (the
data should be examined for over-dispersion to indicate whether a negative
binomial model or perhaps zero inflated models are better suited).
As far as -qreg-, I suggest you read the help file, and even better, the
manual, since qreg has been updated in version 12. The term "quantile" in
the name quantile regression does not mean that it will give you results in
quantiles, but it means that you can indicate which quantile value you are
interested in assessing (ie., 0.5 is the default quantile which is the
median, but you can plug in any value on the continuum from 0-1). Of course,
you can run the model across different quantiles, so you are not limited to
only one level.
Another option (depending on you data and what you are trying to evaluate)
is the -somersd- suite of non-parametric models, which was written by Roger
Newson and comes with its own set of manuals (for somersd, cendif and
censlope) - findit somersd-.
I hope this helps
Date: Fri, 27 Jul 2012 15:33:30 -0400
From: Arnold Behrer <firstname.lastname@example.org>
Subject: st: QREG Question
I have two questions, one related to stats generally and the other to
The first is about how useful quantile regression is in correcting for
skewed data. I have a data set heavily skewed to the right. I have
considered log transforming it but I would rather not because the
skewness is due in large part to zero values that are meaningful in
the context of the regression. I thought that perhaps quantile
regression might be a way of correcting for this skew without dropping
the zero values. Is that true?
The second question is simply whether the Qreg command in Stata runs a
normal quantile regression - splitting the dependent into quantles?
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