Discriminant
Analysis
We are interested in the relationship between a group of
independent variables and one categorical variable. We would like to know how
many dimensions we would need to express this relationship. Using this
relationship, we can predict a classification based on the independent
variables or assess how well the independent separate the categories in the
classification. Discriminant Function Analysis (DA) undertakes the same task as
multiple linear regressions by predicting an outcome.
Major underlying
Assumptions of DA are:
- · Each of the allocations for the dependent categories in the initial classification are correctly classified.
- · The observations are a random sample.
- · Each predictor variable is normally distributed.
- · The groups or categories should be defined before collecting the data.
The purposes of Discriminant
Analysis(DA):
- · To determine the most parsimonious way to distinguish between groups.
- · To classify cases into groups. Statistical significance tests using chi square enable you to see how well the function separates the groups.
- · To test theory whether cases are classifi ed as predicted.
- · To investigate differences between groups on the basis of the attributes of the cases, indicating which attributes contribute most to group separation. The descriptive technique successively identifies the linear combination of attributes known as canonical discriminant functions (equations) which contribute maximally to group separation.
Classification table
Classification phase is the final phase. The classification
is simply a table in which the rows are the observed categories of the
dependent and the columns are the predicted categories. When prediction is
perfect all cases will lie on the diagonal. The percentage of cases on the
diagonal is the percentage of correct classifications.
Rahul Rauniyar
14038
Group-A
Operations Batch
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