Day-9- Team F(Rohan)

What is Discriminant Analysis

Discriminant Function Analysis (DA) undertakes the same task as multiple linear regression by predicting an outcome. However, multiple linear regressions is limited to cases where the dependent variable on the Y axis is an interval variable so that the combination of predictors will, through the regression equation, produce estimated mean population numerical Y values for given values of weighted combinations of X values. But many interesting variables are categorical, such as political party voting intention, migrant/non-migrant status, making a profi t or not, holding a particular credit card, owning, renting or paying a mortgage for a house, employed/unemployed, satisfi ed versus dissatisfi ed employees, which customers are likely to buy a product or not buy, what distinguishes Stellar Bean clients from Gloria Beans clients, whether a person is a credit risk or not,

DA is used when:

The dependent is categorical with the predictor IV’s at interval level such as age, income,  attitudes, perceptions, and years of education, although dummy variables can be used as predictors as in multiple regression. Logistic regression IV’s can be of any level of measurement. 

There are several purposes of DA:

• 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.
• Predictive DA addresses the question of how to assign new cases to groups.
•  The DA function uses a person’s scores on the predictor variables to predict the category to which the individual belongs.
• 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 classified as predicted

Discriminant analysis uses a collection of interval variables to predict a categorical variable that may be a dichotomy or have more than two values. The technique involves  finding a linear combination of independent variables (predictors) – the discriminant function – that creates the maximum difference between group memberships in the categorical dependent variable.  Stepwise DA is also available to determine the best combinations of predictor variables.  Thus discriminant analysis is a tool for predicting group membership from a linear combination of variable

- By Rohan Kulkarni 

Team F