Inferential statistics in OLS regression depend on the assumption that
the distribution of residuals - that is to say, the distribution of
scores on the dependent variable, conditional on the values of the
predictors - is normal. The Central Limit Theorem is not relevant. It
guarantees that parameter estimates will be normally distributed under
certain circumstances. Those circumstances include the residuals being
normally distributed. That is why, if the residuals are skewed, people
transform variables or use estimation techniques other than OLS. David
Greenberg, Sociology Department, New York University.
----- Original Message -----
From: "Michael Cha" <[email protected]>
Date: Sunday, July 21, 2002 4:37 pm
Subject: RE: st: Re: normal distributions
> Dear listers,
>
> Could anyone tell me the relationship between regression
> assumption test (Normality test in particular) and CLT (Central
> Limit Theorem)?
>
> Does CLT imply that we do not have to worry about normality test
> for residuals as far as sample size is large?
>
> Or CLT does not imply anything about the regression assumption
> test?
>
> Thanks in advance,
>
> MCHA,
>
>
>
>
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