Date: Fri, 18 Jul 2008 15:07:20 -0700
Reply-To: "Pirritano, Matthew" <MPirritano@ochca.com>
Sender: "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From: "Pirritano, Matthew" <MPirritano@ochca.com>
Subject: Re: Multicollinearity confusion
In-Reply-To: A<18538040.post@talk.nabble.com>
Content-Type: text/plain; charset="us-ascii"
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.
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