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.
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.