Given a set of independent variables, discriminant analysis attempts to find linear combinations of those variables that best separate the groups of cases. These combinations are called discriminant functions and have the form displayed in the equation.

The procedure automatically chooses a first function that will separate the groups as much as possible. It then chooses a second function that is both uncorrelated with the first function and provides as much further separation as possible. The procedure continues adding functions in this way until reaching the maximum number of functions as determined by the number of predictors and categories in the dependent variable.

The discriminant model has the following assumptions:

- The predictors are not highly correlated with each other.
- The mean and variance of a given predictor are not correlated.
- The correlation between two predictors is constant across groups.
- The values of each predictor have a normal distribution.