In today’s class we were introduced to the concept of Discriminant analysis. The main purpose of a discriminant function analysis is to predict group membership based on a linear combination of the interval variables. The procedure begins with a set of observations where both group membership and the values of the interval variables are known. The end result of the procedure is a model that allows prediction of group membership when only the interval variables are known.
A second purpose of discriminant function analysis is an understanding of the data set, as a careful examination of the prediction model that results from the procedure can give insight into the relationship between group membership and the variables used to predict group membership. The concept was explained to us with the help of an example “Bank Loan” where the dependent variable was “previously defaulted” and independent variable was (age, level of education, years at current address etc). we did a regression analysis to find out whether an individual will default or not. A score was computed which was the base to our conclusion whether an individual will default or not.
There are various applications of discriminant analysis. For example bankruptcy prediction, face recognition etc. One application of discriminant analysis could be to assess whether patients recovered from a coma or not based on combinations of demographic and treatment variables. The predictor variables might include age, sex, general health, time between incident and arrival at hospital, various interventions, etc.
In this case the creation of the prediction model would allow a medical practitioner to assess the chance of recovery based on observed variables. The prediction model might also give insight into how the variables interact in predicting recovery.
Autor name: Varun Sareen
Group name: finance_3