Introduction

Discriminant analysis is a technique for classifying a set of observations into predefined classes. The purpose is to determine the class of an observation based on a set of variables known as predictors or input variables. The model is built based on a set of observations for which the classes are known. This set of observations is sometimes referred to as the training set. Based on the training set , the technique constructs a set of linear functions of the predictors, known as discriminant functions, such that 

L = b₁x₁ + b₂x₂ + … + b_nx_n + c , where the b's are discriminant coefficients, the x's are the input variables or predictors and c is a constant.

These discriminant functions are used to predict the class of a new observation with unknown class. For a k class problem k discriminant functions are constructed. Given a new observation, all the k discriminant functions are evaluated and the observation is assigned to class i if the i^th discriminant function has the highest value.

See also

• Using Discriminant Analysis in XLMiner™

• Example - Discriminant Analysis