Furthermore discriminant analysis is used to make a prediction model for group membership based on the characteristics observed for each case. This procedure will produce a discriminant function based on linear combinations derived from predictor or free variables that can produce the best differences between the groups analyzed. All functions are made from a sample of all cases for known group membership. These functions can be applied to new cases that have measurements for all independent variables but have unknown group membership.
The main purpose of using discriminant analysis is to see linear combinations. This means to study the direction of differences - differences that exist within a group so that a linear combination is found in all independent variables. This linear combination is seen in the discriminant function, namely the differences in the group average. If you use this technique, in practice researchers have a basic task to reduce the coefficients of the discriminant function (straight line). For example: Types of train customers in general can be divided into two, namely those who use executive and business / economic train services. To make this classification, diskriman analysis procedures can be used so that we can find out the factors that influence the making of these classifications. The purpose of conducting this classification is that we can find out whether the grouping is significant or not. This means that groups that use executive train services are truly different from groups that use business / economic classes.
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