When the test on normality revealed non-normality of dep variable,
most often, we do log transformation to improve the normaliy before we
do regression. Why we need to do the
transformation for regression analysis? Any help will be much
appreciated.
Frank

Frank wrote:
> When the test on normality revealed non-normality of dep variable,
> most often, we do log transformation to improve the normaliy before we
> do regression. Why we need to do the
> transformation for regression analysis? Any help will be much
> appreciated.
> Frank

In linear regression, it is the residuals (not Y) that should be 
approximately normal.  But even so, statistical tests of normality 
are not very helpful when they are used to test the assumption of 
some parametric procedure.  The problem is that they have too 
little power when the sample size is small (which is when 
normality is most necessary), and too much power when sample sizes 
are large (and normality is less important due to the central 
limit theorem).  In other words, tests of normality fail to detect 
important departures from normality when the sample size is small, 
and they detect unimportant departures from normality when the 
sample size is larger.

-- 
Bruce Weaver
bweaver@lakeheadu.ca
http://sites.google.com/a/lakeheadu.ca/bweaver/
"When all else fails, RTFM."