Discriminant group. Assumptions: 1. The groups formed are mutually

Discriminant analysis addresses the
situation of a non-metric dependent variable. In this type of situation, the
researcher is interested in the prediction explanation of the relationship that
affect the category in which an object is located, such as why a person is or
is not a customer, or if a firm will succeed or fail.

Formulation of discriminant analysis is:

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Y1= X1 + X2 +…………. + Xn

Where,

Y1 –
dependent non-metric variable

X1, X2… Xn – Independent metric variables

The objectives for applying discriminant
analysis should further clarify its nature. It can address any of the following
research objectives:

Determining whether statistically
significant difference exist between the average score profiles on a set
of variables for two (or more) a priori defined groups.
Determining
which of the independent variables most account for the differences in the
average score profiles of the two or more group.

Assumptions:

1. The groups formed are mutually exclusive
and the group sizes are not different.

2. Independent variable’s variance
structure are same within each and every group of the

dependent variable.

Fallacies
are casually scattered.

The
purpose of the multi discriminant analysis is to explore differences among
groups, to eliminate the variables which are not or very less connected to
group variance, to categorize the cases into groups and to experiment theory by
watching whether cases are categorized as predicted. (George
H. Dunteman (1984).

 

Discriminant
analysis determines the optimal amalgamation of variables. Hence, the 1st
function gives the maximum discrimination between groups, the 2nd
gives the second most so on and so forth. If a more number of independent
variables are chosen and a functional subset has to be chosen for forecasting
the dependent variable, then multi discriminant analysis is used. (Morrison, D.F. 1967.)