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Dilfraz Singh, Lovely Professional University Unit 14: Multivariate Analysis

Unit 14: Multivariate Analysis Notes

CONTENTS
Objectives
Introduction
14.1 Discriminant Analysis

14.2 Factor Analysis
14.3 Cluster Analysis
14.3.1 Cluster Analysis on Three Dimensions
14.4 Conjoint Analysis

14.5 Multidimensional Scaling (MDS)
14.5.1 Types of MDS
14.6 Summary
14.7 Keywords

14.8 Review Questions
14.9 Further Readings

Objectives

After studying this unit, you will be able to:
 Explain the multiple regressions;
 Discuss the discriminant analysis and conjoint analysis;
 Explain the factor analysis and cluster analysis;
 Describe the Multidimensional Scaling (MDS).

Introduction

As the name indicates, multivariate analysis comprises a set of techniques dedicated to the
analysis of data sets with more than one variable. Several of these techniques were developed
recently in part because they require the computational capabilities of modern computers.
Multivariate analysis (MVA) is based on the statistical principle of multivariate statistics, which
involves observation and analysis of more than one statistical variable at a time. In design and
analysis, the technique is used to perform trade studies across multiple dimensions while taking
into account the effects of all variables on the responses of interest. Sometimes, the marketers
will come across situations, which are complex involving two or more variables. Hence,
bi-variate analysis deals with this type of situation. Chi-Square is an example of bi-variate
analysis. In multi-variate analysis, the numbers of variables to be tackled are many.

Example: The demand for television sets may depend not only on price, but also on the
income of households, advertising expenditure incurred by TV manufacturer and other similar
factors. To solve this type of problem, multivariate analysis is required.

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