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What is Discriminant Analysis?

Discriminant Analysis is somewhat similar to regression analysis. We can use dependent or independent variables to predict dependent variables in both techniques. In this, the dependent variable is categorical, not metric. Users can utilize this to classify people or objects into two or more groups based on their characteristics.

Examples

1. based on some demographic or psychographic characteristics of the consumers, we can predict or identify preferences for the brand name or change in brand preferences.

2. Scores in the selection test can be a criteria to select MBA students for the post of salesperson or any other post.

The discriminant analysis may depend on or require data on all the independent variables and the categories to which each object belongs.

For example: the selection of a salesperson, whether each salesperson was selected or rejected for a given set of scores in selection tests

Then, built and tested a discriminant model for usefulness based on:

1. Its significance

The low value of Wilk’s Lambda: High significance

The F test should show a p-value of less than .05

2. Which variables are relatively better at predicting the dependent?

We can directly judge this by examining the standardized coefficients of the independent variables.

The larger the absolute value of the standardized coefficient, the better the predictive or explanatory power of the variable.

3. The number of data points from the original dataset the model classifies correctly.

Closer to 100 is better

This is a simple explanation of the concept of Discriminant Analysis.