"The presentation is always clear and several examples and figures facilitate an easy understanding of all the techniques. The book can be used as a textbook in advanced undergraduate courses in multivariate analysis, and can represent a valuable reference manual for biologists and engineers working with multivariate datasets." -Fabio Rapallo, Zentralblatt MATH 1296

• Introduction. Linear Regression Models. Nonlinear Regression Models. Logistic Regression Models. Model Evaluation and Selection. Discriminant Analysis. Bayesian Classification. Support Vector Machines. Principal Component Analysis. Clustering. Appendices. Bibliography. Index.