"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"This is an excellent textbook for upper-class undergraduate and graduate level students. The prerequisites are an introductory probability and statistics and linear algebra courses. To aid the student in the understanding and use of vector and matrix notations, and to emphasize that importance, the author appropriately uses the algebraic notation accompanied by the vector and matrix notations when needed; additionally, the accompanying geometrical interpretation are presented in clear diagrams. The writing style is crisp and clear. A pleasant format that the author used is to summarily review relevant topics in a narrative style to pave the way into a new topic. The textbook is accessible to students and researchers in the social sciences, econometrics, biomedical, computer and data science fields. This is the kind of textbook that a student or professional researcher will consult many times."—Stephen Hyatt, International Technological University

• 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.

"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"This is an excellent textbook for upper-class undergraduate and graduate level students. The prerequisites are an introductory probability and statistics and linear algebra courses. To aid the student in the understanding and use of vector and matrix notations, and to emphasize that importance, the author appropriately uses the algebraic notation accompanied by the vector and matrix notations when needed; additionally, the accompanying geometrical interpretation are presented in clear diagrams. The writing style is crisp and clear. A pleasant format that the author used is to summarily review relevant topics in a narrative style to pave the way into a new topic. The textbook is accessible to students and researchers in the social sciences, econometrics, biomedical, computer and data science fields. This is the kind of textbook that a student or professional researcher will consult many times."—Stephen Hyatt, International Technological University

• 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.

"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"This is an excellent textbook for upper-class undergraduate and graduate level students. The prerequisites are an introductory probability and statistics and linear algebra courses. To aid the student in the understanding and use of vector and matrix notations, and to emphasize that importance, the author appropriately uses the algebraic notation accompanied by the vector and matrix notations when needed; additionally, the accompanying geometrical interpretation are presented in clear diagrams. The writing style is crisp and clear. A pleasant format that the author used is to summarily review relevant topics in a narrative style to pave the way into a new topic. The textbook is accessible to students and researchers in the social sciences, econometrics, biomedical, computer and data science fields. This is the kind of textbook that a student or professional researcher will consult many times."—Stephen Hyatt, International Technological University

• 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.