User Guide

432
Chapter 28
If these variables are useful for discriminating between the two climate zones, the
values of D will differ for the temperate and tropic countries. If you use a stepwise
variable se
lection method, you may find that you do not need to include all four
variables in the function.
Statistics. For each variable: means, standard deviations, univariate ANOVA. For
each analys
is: Box’s M, within-groups correlation matrix, within-groups covariance
matrix, separate-groups covariance matrix, total covariance matrix. For each
canonical discriminant function: eigenvalue, percentage of variance, canonical
correlati
on, Wilks’ lambda, chi-square. For each step: prior probabilities, Fisher’s
function coefficients, unstandardized function coefficients, Wilks’ lambda for each
canonical function.
Data. The
grouping variable must have a limited number of distinct categories, coded
as integers. Independent variables that are nominal must be recoded to dummy or
contrast variables.
Assumpti
ons.
Cases should be independent. Predictor variables should have a
multivariate normal distribution, and within-group variance-covariance matrices
should be equal across groups. Group membership is assumed to be mutually
exclusi
ve (that is, no case belongs to more than one group) and collectively exhaustive
(that is, all cases are members of a group). The procedure is most effective when
group membership is a truly categorical variable; if group membership is based on
values
of a continuous variable (for example, high IQ versus low IQ), you should
consider using linear regression to take advantage of the richer information offered by
the continuous variable itself.
Figure 28-1
Discriminant analysis output
1.002 100.0 100.0 .707
Function
1
Eigenvalue
% of
Variance
Cumulative
%
Canonical
Correlation
Eigenvalues
.499 31.934 4 .000
Test of
Function(s)
1
Wilks'
Lambda
Chi-square df Sig.
Wilks' Lambda