Discriminant analysis (DA) is a multivariate statistical method used for two purposes: separation of observations into two or more distinct groups and classification of new observations into known groups. In DA, the categorical variables or groups are the dependent variables and the responses are the independent variables, so it is the reverse of a multivariate analysis of variance (MANOVA). Since the two procedures are computationally similar, the same assumptions that apply to MANOVA also apply to DA. Briefly, the assumptions are that the data are normally distributed and the variance/covariance matrices are homogenous across groups. DA is also sensitive to the presence of outliers and multicollinearity among the independent variables.