Discriminant Analysis

Discriminant Analysis

The primary goal of this method is to discover which variables have the best ability of discriminating between two or more known groups in your data. Discriminant analysis may also be used to build predictive analytics models.


  • Introduction to Discriminant Analysis
  • Context of Use & Objective
  • Data Structure Required
  • Principle of Discriminant Analysis
  • Univariate Discrimination
  • Multivariate Discrimination
  • Applications
  • Discriminant Analysis for Predictive Purposes
  • Model Validation
  • Combining Discriminant Analysis with Cluster Analysis
  • Specific Issues and Limitations
  • Alternatives to Discrimimant Analysis
  • Software Packages for Discriminat Analysis
  • Summary
This module is intended for all scientific staff who collect large datasets and who wish to graphically summarize them as well as identify the most discriminant measurements for reforming known groups of objects or individuals. This workshop introduces the important concepts in statistics and data analysis. It assumes that participants have no previous knowledge of statistics or that they have not used it for a long time. A working knowledge of the Analysis of Variance (ANOVA) is not mandatory but desirable.
Upon completion of this module, participants will be able :
    • To understand the context of use for discriminant analysis
    • To understand what the underlying principle and the assumptions of the technique are
    • To interpret statistical software output
    • To use discriminant analysis for predictive purposes
    • To validate and interpret the discriminant functions

Recommended Duration: 1 day(s)

Course Materials:


Case Studies: