Darth is correct. Here’s the relevant quote from a standard statistics text.
Applied Regression Analysis 2nd Edition – Draper and Smith pages 22 and 23
“[with regard to regression] Up to this point we have made no assumptions at all that involve probability distributions. A number of specified algebraic calculations have been made and that is all. We now make the basic assumptions that for a model of Y = fn(Xi +e) (I can’t write epsilon using this platform so I’m using “e” in its place)
1. e is a random variable with mean zero and variance sigma**2.
2. e(i) and e(j) are uncorrelated such that cov(e(i),e(j)) = 0
3. e(i) is a normally distributed random variable, with mean 0 and variance sigma**2″
There are no other assumptions/requirements.
The need for approximate normality in the residuals is because it is the residuals that inform the correctness of the t and F tests used for assessing term significance. In order to address the issue of approximate normality you will need to run a residual analysis – and when you do please follow the guidelines for running a proper analysis (Chapter 3 of the above book has the details) this means assessing the residuals graphically – not just dumping them into some test for normality.
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