Jean H writes:
> > "Yeah, sorta."
> >
> Factor analysis is a statistical method used to explain variability
> among observed random variables in terms of fewer unobserved random
> variables called factors. The observed variables are modeled as linear
> combinations of the factors, plus "error" terms. The information gained
> about the interdependencies can be used later to reduce the set of
> variables in a dataset. Factor analysis originated in psychometrics, and
> is used in behavioral sciences, social sciences, marketing, product
> management, operations research, and other applied sciences that deal
> with large quantities of data.
>
> Factor analysis is often confused with principal components analysis.
> The two methods are related, but distinct, though factor analysis
> becomes essentially equivalent to principal components analysis if the
> "errors" in the factor analysis model are assumed to all have the same
> variance.
> http://en.wikipedia.org/wiki/Factor_analysis
With all due respect, Jean, I think Coyote comes to the point
a bit quicker. ;-)
Cheers,
David
--
David Fanning, Ph.D.
Fanning Software Consulting, Inc.
Coyote's Guide to IDL Programming: http://www.dfanning.com/
Se****e ma de ni thui. ("Perhaps thou speakest truth.")
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