> 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
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.
"When all else fails, RTFM."