Logistic regression has several advantages over discriminant analysis:
* it is more robust: the independent variables don’t have to be normally distributed, or have equal variance in each group
* It does not assume a linear relationship between the IV and DV
* It may handle nonlinear effects
* You can add explicit interaction and power terms
* The DV need not be normally distributed.
* There is no homogeneity of variance assumption.
* Normally distributed error terms are not assumed.
* It does not require that the independents be interval.
* It does not require that the independents be unbounded.
With all this flexibility, you might wonder why anyone would ever use discriminant analysis or any other method of analysis. Unfortunately, the advantages of logistic regression come at a cost: it requires much more data to achieve stable, meaningful results. With standard regression, and DA, typically 20 data points per predictor is considered the lower bound. For logistic regression, at least 50 data points per predictor is necessary to achieve stable results.