[snicker7]

Despite what the article claims, normality is not actually an assumption of linear regression. It is "required" for doing F-tests (the F-distribution being related to the normal distribution), but it is not required for proving that the regression coefficients are consistent.

[knoepfle]

It's actually not even required for that! See http://davegiles.blogspot.com/2011/08/being-normal-is-option... which cites King (1980):

> If the error vector in our regression model follows any distribution in the family of Elliptically Symmetric distributions, then any test statistic that is scale-invariant has the same null and alternative distributions as they have when the errors are normally distributed.

[knoepfle]

Note also that any distributional assumptions are really only necessary for inference (i.e., tests and confidence intervals) in finite samples (read: small samples); the central limit theorem guarantees the tests work asymptotically, so you're usually going to be fine.

Most of the attention paid to distributional assumptions in regression is wasted, and would be better spent on really thinking through the assumed moment conditions underlying the estimator.