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Date:         Sun, 11 Dec 2005 07:34:35 -0500
Reply-To:     Peter Flom <flom@NDRI.ORG>
Sender:       "SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>
From:         Peter Flom <flom@NDRI.ORG>
Subject:      Re: Normality Assumption for Pearson Product Moment Correlation
Comments: To: davidlcassell@MSN.COM
Content-Type: text/plain; charset=US-ASCII

> David L Cassell <davidlcassell@MSN.COM> >>> wrote

<<< There's no normality assumption at all in the *calculation* of the Pearson r. The assumption comes in later, when you use that r and do the hypothesis test of H0: rho = 0. If you assume bivariate normality (and independence, and equally distributed) then r is the best way to assess the strength of the relationship between the two variables. That's because the bivariate normality guarantees a linear relationship between the variables, and r is a parametric measure of rho. >>>

I never heard before that bivariate normality assures a linear relationship, and I'm having a little trouble figuring out why it should.....

any pointers to articles or whatever discussing this?

Usually, I just stress that correlation is just a measure of LINEAR relationship, and then do scatterplot to see if it looks like there is a NONlinear relationship, so this isn't really critical, but I always like to learn things like this....



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