Page 261 - DMGT209_QUANTITATIVE_TECHNIQUES_II

Quantitative Techniques-II

Notes

Notes Multiple-variate analysis: This can be studied under:
1. Discriminant analysis
2. Factor analysis
3. Cluster analysis
4. Conjoint analysis
5. Multidimensional scaling.

14.1 Discriminant Analysis

In this analysis, two or more groups are compared. In the final analysis, we need to find out
whether the groups differ one from another.

Example: Where discriminant analysis is used
1. Those who buy our brand and those who buy competitors’ brand.
2. Good salesman, poor salesman, medium salesman.
3. Those who go to Food World to buy and those who buy in a Kirana shop.
4. Heavy user, medium user and light user of the product.
Suppose there is a comparison between the groups mentioned as above along with demographic
and socio-economic factors, then discriminant analysis can be used. One way of doing this is to
proceed and calculate the income, age, educational level, so that the profile of each group could
be determined. Comparing the two groups based on one variable alone would be informative
but it would not indicate the relative importance of each variable in distinguishing the groups.
This is because several variables within the group will have some correlation which means that
one variable is not independent of the other.
If we are interested in segmenting the market using income and education, we would be
interested in the total effect of two variables in combinations, and not their effects separately.
Further, we would be interested in determining which of the variables are more important or
had a greater impact. To summarize, we can say, that Discriminant Analysis can be used when
we want to consider the variables simultaneously to take into account their interrelationship.
Like regression, the value of dependent variable is calculated by using the data of independent
variable.
Z = b x + b x + b x +..............
1 1 2 3 3
Z = Discriminant score
b = Discriminant weight for variable
1
x = Independent variable
As can be seen in the above, each independent variable is multiplied by its corresponding
weightage.
This results in a single composite discriminant score for each individual. By taking the average
of discriminant score of the individuals within a certain group, we create a group mean. This is
known as centroid. If the analysis involves two groups, there are two centroids. This is very
similar to multiple regression, except that different types of variables are involved.
Application: A company manufacturing FMCG products introduces a sales contest among its
marketing executives to find out “How many distributors can be roped in to handle the company’s
product”. Assume that this contest runs for three months. Each marketing executive is given
target regarding number of new distributors and sales they can generate during the period. This

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