Multivariate Analysis 3e

This is a clearly written text on multivariate analysis. As there are many excellent texts both involving theory and applications, using geometric approaches as in Eaton's biik and algebraic approaches as in Anderson's it is important to know the differences and the avanatges and disadvantages to each. Rencher's book has a lot of good applications and is very applications oriented in its approach. It does not cover structural equation models. The field of multivariate statistics is very broad and topics such as classification (or pattern recognition), structural equation models and factor analysis are often better understood by reading specialized texts such as McLachlan for pattern recognition, Bollen for structural equations and Harman for factor analysis. Rencher's book is modern, well-written and very much up to date.

The book was brand new. It arrived to me in perfect conditions although having been travelled from USA to Europe. The shipment was fast and cheap. I will repeat. Thanks!

exactly what it says on the cover, good dealer, do recommend (the seller) hte book s good too, however multivariate analysis is not easy...

This textbook provides a comprehensive introduction to multivariate analysis. Although concise, the explanation is clear and intuitive, of course, some preliminary knowledge of statistics and linear algabra is necessary to follow this book.

This book is great! Some derivations and occasional proofs are included in the text, but it has clearly been written with the applied researcher in mind. In the author's words he has provided "careful intuitive explanations of the concepts and [has] included many insights typically available only in journal articles or in the minds of practitioners" with his primary objective being "clarity of exposition." I have found it to be very easy to read with numerous helpful examples (the data sets and SAS command files for which are available via an ftp site). This brand new version includes new chapters on cluster analysis, multidimensional scaling, correspondence analysis, and biplots.