Page 291 - DMGT404 RESEARCH_METHODOLOGY

Unit 13: Multivariate Analysis

Further, we have Notes
å U å U å U
X = 1 + 65 = 65,X = 2 + 55 = 55 and X = 3 + 30 = 30
1 2 3
n n n

!
Caution The above method should be used when mean of all the variables are integers.

Alternative Method
The coefficients of the regression equation X = a + b X + b X can also be obtained by
1c 1.23 12.3 2 13.2 3
simultaneously solving the following normal equations:
SX = na + b SX + b SX
1 1.23 12.3 2 13.2 3
2
SX X = a SX + b SX + b SX X
1 2 1.23 2 12.3 2 13.2 2 3
SX X = a SX + b SX X + b SX 2
1 3 1.23 3 12.3 2 3 13.2 3
Self Assessment

Fill in the blanks:
1. Regression coefficient is independent of change of .......................

2. In the case of ………………regression, one variable is affected by a linear combination of
another variable.
3. ……………..analysis is based on the statistical principle of multivariate statistics, which
involves observation and analysis of more than one statistical variable at a time.

13.2 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

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