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.
In simple words, the analysis helps into finding which item or object belongs to a particular group or classification based on certain characteristics. It differs from group building techniques such as cluster analysis in that the classifications or groups to choose from must be known in advance.
To take an example, let us assume that we have data on 80 students in Business Analytics Class. We have data on number of students who want a job in Analytics and Data Modelling and number of students who want in Sales and Distribution. We need to predict group membership by looking at independent variables which may include: Students with engineering background, age, gender, number of work experience years.
The discriminant function analysis thus helps to predict group membership when only independent variables are known. It shows the relationship between the dependent variable (Students interested in analytics job and students interest in Sales job) and interval variables (Age, gender, number of work experience years, education background etc). The analysis shows that students with engineering background and students with more work experience wanted job in Analytics industry. Also, the students wanting job in sales showed a trend of students who did not have work experience, younger age (22-24 years) and were males.
Thus, this model helps to predict membership for group of students wanting job in Analytics and Sales based on observed variables.
In class, 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.
Another scenario where Discriminant Analysis can be used is to find the variable which predicts the number of students using cell phones in class and students not using. The observed variables can b: Age, academic grades, member of committee, a persona having girlfriend/ boyfriend, etc. Thus, we can find characteristics essential to classify students into two groups: Students using cell phones and students not using cell phones. The relationship established can be used to predict the groups and have proper mix in all classes and avoid sending students of similar group in the same class.
This analysis can also be used to find: The number of customers going for insurance policy after meeting the agent of the company. The essential characteristics include: Gender, Age group, income level, education level, and awareness about insurance, etc. Thus a model can be established to understand the data set and identify characteristics affecting the decision making. Thus, the analysis can be used to assess training needs for agents meeting different class of customers to increase convertibility.
Finance - GROUP 1
Finance - GROUP 1