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Discriminant analysis is a statistical technique widely used in the business world. Graduate Tutor’s operations research tutors or statistics tutors can tutor you in the theory and application of discriminant analysis. Discriminant analysis is used to forecast the outcome of a variety of variables that impact the profitability of a business. Classic examples of the application of discriminant analysis include:

  • Using discriminant analysis to perform a default risk evaluation of loan applicants;
  • Discriminant analysis bench marking of potential job applicants;
  • Classification of customers into different customer segments;
  • Forecasting insurance risk using discriminant analysis;
  • Predicting academic performance from historical data using discriminant analysis;
  • Developing auditing patterns using discriminant analysis;
  • Fraud management using discriminant analysis; etc

Discriminant Analysis homework usually involves setting up one of the above scenarios with background information. Faculty expect discriminant analysis to be done on Microsoft Excel spreadsheets or other software or spreadsheets like Google Docs that have similar spreadsheet modeling features.  Our operations research tutors or statistics tutors can help you understand and set up the discriminant analysis homework case.

Discriminant analysis uses information available in a set of independent variables to predict the value of a discrete or categorical dependent variable. The foundation of discriminant analysis is built on the simple linear regression, multiple linear regression and analysis of variance (ANOVA) techniques developed by Sir Ronald Fisher in 1936. While discriminant analysis was initially developed for dichotomous variables, it is used in a variety of settings today.

Graduate Tutor’s operations research tutors or statistics tutors can tutor you in the theory and application of discriminant analysis. The following is a sample of the topics you can get private tutoring in or homework help with by our statistics tutors.

  • The Two-Group Discriminant Analysis Problem
  • Group Locations and Centroids using Discriminant Analysis
  • Calculating Discriminant Scores
  • The Classification Rule for the Two-Group Discriminant Analysis Problem
  • Refining the Cutoff Value for the Two-Group Discriminant Analysis Problem
  • Classification Accuracy
  • Classifying New Employees using Discriminant Analysis
  • The k-Group Discriminant Analysis Problem
  • Multiple Discriminant Analysis
  • Distance Measures
  • MDA Classification
  • Application of Discriminant Analysis to a variety of problems to reinforce the concepts and implementation in business situations

Other operations research and statistics topics that we can tutor you in include: