Date:         Wed, 17 Dec 2008 07:43:11 -0600
Reply-To:     "Peck, Jon" <peck@spss.com>
Sender:       "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From:         "Peck, Jon" <peck@spss.com>
Subject:      Re: Gram Schmidt orthogonalization procedure
Comments: To: Dulce Subida <dulcesubida@YAHOO.COM.BR>
In-Reply-To:  A<200812171307.mBHBl18i020280@malibu.cc.uga.edu>
Content-Type: text/plain; charset="US-ASCII"

-----Original Message-----
From: SPSSX(r) Discussion [mailto:SPSSX-L@LISTSERV.UGA.EDU] On Behalf Of Dulce Subida
Sent: Wednesday, December 17, 2008 6:07 AM
To: SPSSX-L@LISTSERV.UGA.EDU
Subject: [SPSSX-L] Gram Schmidt orthogonalization procedure

Hi everyone!

I need to get a Gaussian logistic model in SPSS, to predict the
probability of occurence of a species (binary dependent variable) as a
function of an environmental variable (x: continuous independent
variable). I thought I could simply perform the binary logistic
regression, using x and x2 as independent variables in the model. However
in such case I think I will have a severe problem of multicollinearity
(between x and x2). I thought I could orthogonalize both independent
variables in order to surpass that problem. Does anyone know how can I run
the Gram Schmidt orthogonalization procedure in SPSS, or does anyone know
the syntaxis for it?
[>>>Peck, Jon] Orthogonalizing the predictors first is not going to help.  The regression calculations already partial out the variable effects.  Imagine, for example, that your predictors are perfectly correlated.  One would then be zero after orthogonalization, and you still would not get any better coefficient estimates.

The MATRIX procedure can handle this sort of calculation, but the problem you have is not one of calculation.

HTH,
Jon Peck

Thank you a lot for your help!

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