On Fri, 24 Mar 2000 Llorenç Badiella <[EMAIL PROTECTED]> wrote:
> Is it a problem that there exists colinearity between
> variables when performing a Log-Reg? If so, how avoid it?
It can be a problem, or for that matter several problems. Depends on how
severe the collinearity is, among other things. If any predictor is a
linear combination of a set of other predictors, for example, the last
potential predictor among these cannot enter the regression (if the
software is proceeding one variable at a time), or the correlation matrix
(or the variance-covariance matrix) among the predictors cannot be
inverted and a solution cannot be obtained (if the software is proceeding
by inversion of the complete matrix of predictors). If it is merely that
many or all of the predictors are highly intercorrelated with some or all
of the remaining predictors, Rich Ulrich's response indicates some of the
problems you _might_ be encountering.
There are methods of dealing with the problems, but the choice(s)
of method(s) will depend in part on whether the intercorrelations are
reflecting some characteristic(s) of the universe of discourse that you
wish to retain, or are artifacts introduced by the way in which (some of)
the variables were defined.
-- DFB.
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Donald F. Burrill [EMAIL PROTECTED]
348 Hyde Hall, Plymouth State College, [EMAIL PROTECTED]
MSC #29, Plymouth, NH 03264 603-535-2597
184 Nashua Road, Bedford, NH 03110 603-471-7128
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