Discriminant analysis
Discriminant Function Analysis (DA) refers to the process of determining which continuous independent (predictor) variables discriminate between a discrete dependent (response) variable's categories, which can be considered as a reversed Multivariate Analysis of Variance (MANOVA).
This suggests that DA is very similar to logistic regression (see Chapter 6, Beyond the Linear Trend Line (authored by Renata Nemeth and Gergely Toth) and the following section), which is more generally used because of its flexibility. While logistic regression can handle both categorical and continuous data, DA requires numeric independent variables and has a few further requirements that logistic regression does not have:
Normal distribution is assumed
Outliers should be eliminated
No two variables should be highly correlated (multi-collinearity)
The sample size of the smallest category should be higher than the number of predictor values
The number of independent variables should not exceed...
