In article <4l117.324$[EMAIL PROTECTED]>,
Brian MacDonald <[EMAIL PROTECTED]> wrote:
>I am doing a series of analyses using discriminant analysis to predict group
>membership. Several of the variables I am using show distributions that are
>not normal. My question is can these (and for that matter shold they) be
>somehow transformed so that the resulting distribution looks "and presumably
>acts in the analyses) like a normal distribution.
Discriminant analysis, as usually done, is poor without
joint normality and linear comparison functions.
Marginal normality does not imply joint normality, and
any transformation to normality on non-normal data is
likely to destroy linearity.
The assumptions to be made should come from understanding
of the problem, and not by the availability of statistical
methods.
--
This address is for information only. I do not claim that these views
are those of the Statistics Department or of Purdue University.
Herman Rubin, Dept. of Statistics, Purdue Univ., West Lafayette IN47907-1399
[EMAIL PROTECTED] Phone: (765)494-6054 FAX: (765)494-0558
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