Discriminent

1. Discriminant Analysis
 DA is a technique for analyzing data when
the criterion or dependent variable is
categorical and the predictor or
independent variables are interval in
nature.

2. Discriminant Analysis
 DA is sometimes also called
 Discriminant factor analysis
 Canonical discriminant analysis

3. Objectives
 Development of discriminant functions
 Examination of whether significant
differences exist among the groups, in
terms of the predictor variables.
 Determination of which predictor
variables contribute to most of the
intergroup differences
 Evaluation of the accuracy of
classification

4. Multiple Discriminant Analyses
 Discriminant functions
A function like multiple regression but
different in the aspect that the dependent
variable is categorical.

5. Discriminant Analyses
 The discriminant score , or DA score, is
the value resulting from applying a
discriminant function formula to the data
for a given case.

6. Multiple Discriminant Analyses
 Discriminant coefficients are the regression-
like b coefficients in the discriminant function, in
the form D = b1x1 + b2x2 + ... + bnxn + c,
 where D is the latent variable formed by the
discriminant function, the b's are discriminant
coefficients, the x's are predictor variables, and c
is a constant.

7. Multiple Discriminant Analyses
 The structural discriminant function coefficients
are partial coefficients, reflecting the unique
contribution of each variable to the classification
of the criterion variable
 The standardized discriminant coefficients , like
beta weights in regression, are used to assess
the relative classifying importance of the
independent variables

8. Discriminant Analyses
Applications:
Market Research: Market segmentation
Financial Research: Default behavior
Human resources: High performers