Multivariate Statistical Methods: A Primer, Fourth Edition, 4th Edition (Paperback) book cover

Multivariate Statistical Methods

A Primer, Fourth Edition, 4th Edition

By Bryan F.J. Manly, Jorge A. Navarro Alberto

Chapman and Hall/CRC

253 pages | 34 B/W Illus.

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Description

Multivariate Statistical Methods: A Primer provides an introductory overview of multivariate methods without getting too deep into the mathematical details. This fourth edition is a revised and updated version of this bestselling introductory textbook. It retains the clear and concise style of the previous editions of the book and focuses on examples from biological and environmental sciences. The major update with this edition is that R code has been included for each of the analyses described, although in practice any standard statistical package can be used.

The original idea with this book still applies. This was to make it as short as possible and enable readers to begin using multivariate methods in an intelligent manner. With updated information on multivariate analyses, new references, and R code included, this book continues to provide a timely introduction to useful tools for multivariate statistical analysis.

Reviews

"… Multivariate Statistical Methods: A Primer has a fairly standard coverage of available multivariate statistical methods, but stands out in its presentation of these, which is concise, pedagogic, and easy to follow. Each chapter is fairly short, covering only the most essential details, using mathematical formulas only when it is necessary. The stated purpose of the book, to introduce multivariate statistical methods to non-mathematicians while keeping details to a minimum, but still conveying a good idea of what can be done in the area of multivariate statistics, is thus well fulfilled.

The book takes a practical approach to multivariate statistical methods, with illustrations utilizing real, varying data sets from different disciplines, thus making it useful for the applied statistician. … To summarize, this is a very nice book giving a concise not overly technical treatment of multivariate statistical methods that is highly recommended for anyone wanting to have an easy-to-understand overview of this important subject."

—Andreas Rosenblad, Uppsala University, in Journal of Statistical Software, June 2017

"This book is a great choice for an undergraduate or graduate level multivariate statistics course where the students have some previous exposure to statistical methods but don’t need the mathematical foundations of the methods themselves. The authors provide interesting examples with explanations and interpretations without an overuse of mathematical notation, which makes this book accessible to a wide audience. The inclusion of an appendix at the end of each section on using R to conduct multivariate analyses is a useful addition to the book. The strengths of this book are the advice provided from an experienced practitioner and that it provides an introduction to a wide variety of multivariate methods. I used the first edition of this book as a graduate student and the third edition for an undergraduate course I recently taught. It continues to improve with each edition."

Debra L. Hydorn, Professor of Mathematics, University of Mary Washington

"Multivariate Statistical Methods: A Primer (MSM) has always had a special place in the world of teaching as a non-technical introduction to multivariate analysis for those interested in understanding and performing this type of data analysis. Like its previous editions, this new, fourth edition of MSM strives to help the reader develop an intuitive understanding of multivariate data while providing examples and a gentle introduction to its basic mathematical concepts. Readers should take heart to appreciate this approach. It is important that all users, from social scientists to analytics professionals, have a basic conceptual understanding of multivariate methods so that they are able to properly vet, interpret, and use their data analyses. In addition to an overview of each method, MSM provides several useful and interesting multivariate data sets, which it uses throughout the text as examples, and this fourth edition also provides the R code for these examples as an appendix to each chapter. For individuals wanting to develop hands-on experience and an understanding of multivariate methods, the data sets, R code, and discussions of examples throughout the text are invaluable and accessible."

Chad R Bhatti, Predictive Analytics, Northwestern University

"I have been using the third edition of Bryan Manly’s "Multivariate Statistical Methods" in my graduate class on System Analysis for the last three years. Personally, I like this book and I advise my students to continue using it, even after the course is over, as it serves as a compact guide to multivariate statistics. It is a short textbook, but it still nicely covers a variety of different and difficult topics, and it demonstrates the many possible approaches to solving a given problem. The book is quite well organized; it includes some necessary mathematical background such as linear algebra, but also good examples and different sets of interesting data which are used to illustrate different methods. Later these sets are needed to solve problems, which could be used by students to assist their homework assignments. I am looking forward to using this new and improved edition of the book in my courses!"

Alexey L Sadovski, Professor of Mathematics, Texas A&M University-Corpus Christi

Praise for the Third Edition:

"The previous edition (2E) was reviewed by Nemeth (1997), who was enthusiastic about the book's role as 'an excellent, easy-to-read introduction to the analysis of multivariate data'…Her summary continues to work just fine for this new edition…This is a nice book to have around to loan to people who are just getting started in multivariate analysis."

Technometrics, Vol. 47, No. 3, August 2005

Table of Contents

The Material of Multivariate Analysis.

Matrix Algebra.

Displaying Multivariate Data.

Tests of Significance with Multivariate Data.

Measuring and Testing Multivariate Distances.

Principal Components Analysis.

Factor Analysis.

Discriminant Function Analysis.

Cluster Analysis.

Canonical Correlation Analysis.

Multidimensional Scaling.

Ordination.

Epilogue.

Subject Categories

BISAC Subject Codes/Headings:
MAT029000
MATHEMATICS / Probability & Statistics / General