Testing for normality of residuals in a regression model

Hi John,

Your point is well taken.  I was only thinking about the shape of the
distribution, and neglected the cases of, say, symmetric long tailed
distributions.  However, I think I'd still argue that other tools are
probably more useful than normality tests (e.g., robust methods, as you
mentioned).

To take the point a bit further, let's say we test for normality and it's
rejected.  What do we do then?  Well, if the non-normality is caused by
outliers, we can try robust methods.  If not, what do we do?  We can try to
see if some sort of transformation would bring the residuals closer to
normally distributed, but if the interest is in inference on the
coefficients, those inferences on the `final' model are potentially invalid.
What's one to do then?

Also, I was told by someone very smart that fitting OLS to data with
heteroscedastic errors can make the residuals look `more normal' than they
really are...  Don't know how true that is, though.

Best,
Andy