Date: Fri, 18 Jul 2008 23:36:36 -0700 amora_johnny@yahoo.com "SPSSX(r) Discussion" Johnny Amora Re: Multicollinearity confusion To: "Pirritano, Matthew" <97D6F0A82A6E894DAF44B9F575305CC9040FD27B@HCAMAIL03.ochca.com> text/plain; charset=iso-8859-1

Matt,   Can you recommend a reference on the interpretation of nonlinear effect, particularly quadratic amd cubic?

Thanks.

--- On Sat, 7/19/08, Pirritano, Matthew <MPirritano@ochca.com> wrote:

From: Pirritano, Matthew <MPirritano@ochca.com> Subject: Re: Multicollinearity confusion To: SPSSX-L@LISTSERV.UGA.EDU Date: Saturday, July 19, 2008, 6:07 AM

I believe that it is the combination of the linear and squared variable that together give you the curvilinear effect of the variable. You are not interested or able to look only at the linear effect when the quadratic is in the equation. You can only evaluate the squared effect.

matt

Matthew Pirritano, Ph.D. Research Analyst IV County of Orange Medical Services Initiative (MSI) mpirritano@ochca.com (714) 834-6011

-----Original Message----- From: SPSSX(r) Discussion [mailto:SPSSX-L@LISTSERV.UGA.EDU] On Behalf Of jimjohn Sent: Friday, July 18, 2008 2:33 PM To: SPSSX-L@LISTSERV.UGA.EDU Subject: Multicollinearity confusion

I'm a little confused. So, multicollinearity is a problem that can affect our regression results when the independent variables are correlated with each other. But many times, I see regression models like this: y = B0 + B1 *Factor1 + B2 * (Factor1)^squared

So, wouldn't Factor 1 and (Factor 1)^squared be highly correlated, thus resulting in a big collinearity problem? Any ideas why its ok here? Thanks. -- View this message in context: http://www.nabble.com/Multicollinearity-confusion-tp18538040p18538040.ht ml Sent from the SPSSX Discussion mailing list archive at Nabble.com.

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