User Guide

Chapter

Discriminan

tAnalysis

Discriminant analysis is useful for situations where you want to build a predictive

model of gro

up membership based on observed characteristics of each case. The

procedure generates a discriminant function (or, for more than two groups, a set of

discriminant functions) based on linear combinations of the predictor variables that

provide th

e best discrimination between the groups. The functions are generated from

a sample of cases for which group membership is known; the functions can then be

applied to new cases with measurements for the predictor variables but unknown

group mem

bership.

Note: The grouping variable can have more than two values. The codes for the

grouping variable must be integers, however, and you need to specify their minimum

and maxim

um values. Cases with values outside of these bounds are excluded from

the analysis.

Example. On average, people in temperate zone countries consume more calories per

day than

those in the tropics, and a greater proportion of the people in the temperate

zones are city dwellers. A researcher wants to combine this information in a function

to determine how well an individual can discriminate between the two groups of

countr

ies. The researcher thinks that population size and economic information may

also be important. Discriminant analysis allows you to estimate coefficients of the

linear discriminant function, which looks like the right side of a multiple linear

regres

sion equation. That is, using coefficients a, b, c,andd, the function is:

D=a*cl

imate+b*urban+c*population + d * gross domestic product per capita

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