Yesterday, I had written about conjoint analysis. A few days back, when Discriminant analysis was being taught in class, due to some reasons I had to skip the class. So today I thought, I would read on discriminant analysis and would write on that topic.
From what I could understand, the major application area of discriminant analysis is where a distinction between two or three set of objects, or people,, based on knowledge of some of their characteristic is required. For Ex. For the selection process of a job, the admission process of an educational programme in a college, or dividing a group of people into different classes, like buyers and non buyers. Discriminant Analysis can be, and is in fact, used by credit rating agencies to rate the credit risk. In fact being students of finance, we had done an analysis by taking a paper where Risk analysis of SMEs was done using Discriminant Analysis and even a Risk model was developed. This technique can be very handy in deciding and classifying them into good lending risk and bad lending risk.
From what I could make out by going through various articles and blogs on the net, that discriminant analysis is very similar to the multiple regression technique. It also gives quite a similar equation as we get in a regression equation, where Y is the dependent variables and x1 and x2 are the independent variables. K1 and k2 are the unstandardised discriminant function coefficients.
To summarize, we can use linear discriminant analysis when we have to classify objects into two or more groups based on knowledge of some variables related to them. Typically these groups would be users/non-users, potentially successful salesman/potentially unsuccessful salesman, high risk/low risk customers or can be on any such similar lines.
FINANCE - GROUP 1