Multiple Regression and Multiple Classification Analysis

5

Introduction

This chapter examines a model of multivariate analysis, involving simultaneous consideration of several independent (predictor or explanatory) variables and one dependent variable, where the objectives of analysis are:

(i)    To know how well all the independent variables together explain variation in the dependent variable.
(ii)   To know how well each independent variable is related to the dependent variable, either considering or ignoring the effects of other independent variables.

The following data analysis situations can be visualized, depending upon the measurement properties of the dependent and independent variables.

Dependent variable One	Independent variables Several	Statistical techniques
Interval scale	Interval scale	Multiple Regression
Interval scale	Nominal	Multiple Classification Analysis
Dichotomous, Polytomous	Nominal	Multiple Classification Analysis

From a statistical point of view, these techniques (Multiple Regression and Multiple Classification Analysis) assume that the dependent variable is predictable from an additive combination of the predictor variables. In other words, they assume that the average score on the dependent variable for a given set of individuals (objects or cases) is predictable by adding the effects of several predictors.