You might be interested in this paper
http://gking.harvard.edu/files/abs/making-abs.shtml
Though not clear from the abstract, the paper
devotes a lot of attention to interpreting coefficients
when the dependent variable is log transformed.
The authors also distribute the package -clarify-
net from http://gking.harvard.edu/clarify
that goes with the paper. I've never used it, but
the basic idea is to use simulation to get coefficients
in natural units.
cheers,
J
Ashwin Ananthakrishnan wrote:
Hi,
I'm having some trouble interpretting the linear regression
co-efficients for log transformed variables.
I have outcomes (such as length of stay or costs) that are not
normally distributed, so I'm including the log transformed (now
normal) variables as the outcome measures in linear regression
models.
But I'm not really sure how to interpret the resulting co-efficients.
Do they represent a % change in outcome for a defined change in a
predictor variable?
Just for example, suppose I'm modelling length of stay against gender
(male 0 female 1).
Without log transformation, if I get a linear regression co-efficient
of 0.6, I can say that females have a 0.6 days longer stay.
But if I use log (length of stay) as the outcome and get a
co-efficient 0.2 for the same linear regression model, how do I
interpret this?
Thanks.
* * For searches and help try: *
http://www.stata.com/help.cgi?search *
http://www.stata.com/support/statalist/faq *
http://www.ats.ucla.edu/stat/stata/
*
* For searches and help try:
* http://www.stata.com/help.cgi?search
* http://www.stata.com/support/statalist/faq
* http://www.ats.ucla.edu/stat/stata/
