Discriminant Analysis

Three birds are pecking at the ground while Garfield pretends not to notice them. Some feathers are scattered on the ground & Garfield still pretends not to notice the remaining two birds. More feathers are scattered on the ground. The last remaining bird says scornfully to Garfield, "You must think birds aren't very observant," to which Garfield replies, "On an average, two out of three of them aren't."

Welcome to the world of numbers, stats, figures (no, not the one you’re fantasizing about right now) and god knows what all. Yes, it will be useful in the future. Yes, you probably will still not take it seriously. 

What is Discriminant Analysis? 

Discriminant analysis is useful for situations where you want to build a predictive model of group 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 the 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 membership.

Eg. :  Whether people recovered from a coma or not based on combinations of demographic and treatment variables. The predictor variables might include age, sex, general health, time between incident and arrival at hospital, various interventions, etc. In this case the creation of the prediction model would allow a medical practitioner to assess the chance of recovery based on observed variables. The prediction model might also give insight into how the variables interact in predicting recovery.

Use of Discriminant Analysis ?

Discriminant analysis analyzes the dependency relationship, whereas factor analysis and cluster analysis address the interdependency among variables.

In marketing, discriminant analysis is used to determine the factors which distinguish different types of customers and/or products on the basis of surveys or other forms of collected data.

It can also be used for market segmentation and predicting group membership because of its ability to classify individuals or experimental units into two or more uniquely defined populations.

Product research – Distinguish between heavy, medium, and light users of a product in terms of their consumption habits and lifestyles

Perception/Image research – Distinguish between customers who exhibit favourable perceptions of a store or company and those who do not

Advertising research – Identify how market segments differ in media consumption habits

Direct marketing – Identify the characteristics of consumers who will respond to a direct marketing campaign and those who will not

(Source: http://www.cob.sjsu.edu/webb_k/B231A/Discrim.htm)

Author: Mudit Bhandari 13023

Marketing Group 5