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Re: st: getting realistic fitted values from a regression

From   Maarten buis <>
Subject   Re: st: getting realistic fitted values from a regression
Date   Thu, 22 Jul 2010 07:08:01 +0000 (GMT)

--- On Wed, 21/7/10, Woolton Lee wrote:
> I have estimated a regression (OLS) using log of patient
> travel distance to a hospital predicted by patient, hospital
> and area characteristics.  I am going to report the results
> as marginal effects that I've computed by obtaining
> predictions from my estimated regression computed by fixing
> some variables and keeping others at their original values.
>  However after I compute the predictions, I am getting
> unrealistically large numbers.  When I examined the regression
> residuals it looks as though the obs with unrealistic fitted
> values have larger residuals.  Is there a way to adjust the
> regression to better account for this problem?

If you want to predict the travel distance you should use 
-glm- with -link(log)- option rather than use -regress- on
a log transformed dependent variable. The difference is that
with the former you are modeling log(E(y)), while in the latter
you are moddeling E(log(y)). If you want to backtransform your
predictions using the antlog transformation you will get 
exp(log(E(y))) = E(y) for the -glm- command, while after -regress
you get exp(E(log(y))) != E(y). A nice discussion on this issue
can be found in:

Nicholas J. Cox, Jeff Warburton, Alona Armstrong, Victoria J. Holliday 
(2007) "Fitting concentration and load rating curves with generalized
linear models" Earth Surface Processes and Landforms, 33(1):25--39.

There exist approximations you can use after -regress- to fix
this problem, by why try to fix a problem if you can easily prevent

Hope this helps,

Maarten L. Buis
Institut fuer Soziologie
Universitaet Tuebingen
Wilhelmstrasse 36
72074 Tuebingen


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