The Material of Multivariate Analysis.
Displaying Multivariate Data.
Tests of Significance with Multivariate Data.
Measuring and Testing Multivariate Distances.
Principal Components Analysis.
Discriminant Function Analysis.
Canonical Correlation Analysis.
"... 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
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