Applied Multivariate Statistical Analysis

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Product Description

Most of the observable phenomena in the empirical sciences are of a multivariate nature.  In financial studies, assets are observed simultaneously and their joint development is analysed to better understand general risk and to track indices.� In medicine recorded observations of subjects in different locations are the basis of reliable diagnoses and medication. � In quantitative marketing consumer preferences are collected in order to construct models of consumer behavior.  The underlying data structure of these and many other quantitative studies of applied sciences is multivariate.� Focusing on applications this book presents the tools and concepts of multivariate data analysis in a way that is understandable for non-mathematicians and practitioners who need to analyze statistical data.� The book surveys the basic principles of multivariate statistical data analysis and emphasizes both exploratory and inferential statistics.� All chapters have exercises that highlight applications in different fields.

The third edition of this book on Applied Multivariate Statistical Analysis offers the following new features

• A new Chapter on Regression Models has been added
• All numerical examples have been redone, updated and made reproducible in MATLAB or R, see www.quantlet.org for a repository of quantlets.

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Applied Multivariate Statistical Analysis Review

This is probably the best applied statistics book I have ever read. It is not one of the "for dummies" book, it does use some linear algebra and requires some knowledge of elementary statistics, but at the same time it is very clear and understandable. I think this is the only reasonable approach - whatever you are told, you cannot understand statistics if you are not prepared - you can't run before you learn to walk. If you buy a "statistics for dummies" you will only waste your money - you will learn a few names of statistical methods (and possibly what to click in your favourite stats program) but you will not be able to use them.

The authors start with a few examples, then lay out the formalism, and then use it in introducing various methods and techniques. The level of generality is not very high and you can read the book without the knowledge of, say, modern integration theory, yet it is sufficient for all the APPLIED problems that the reader is likely to meet in his/her work. (If you want to publish papers in AMSTAT journals you will have to learn more)

A potential strength of this book is the electronic version which you can download using the code given at the end of the book, but I haven't done this so far. I assume that if you travel a lot you can carry the book on your laptop instead of your backpack. I have downloaded Xplore and find it quite nice, however, my stats system of choice is R, so I used this instead.

There are some minor problems: for example there are some typos (some of them quite serious) and the end of chapter problems are not challenging enough (most can be done by inspection or by plugging numbers into Xplore). Speaking of the problems, the authors say that there is a solution manual, but it does not seem possible to get hold of it in any way. Still, the problems are so simple, that no solutions manual seems necessary.

All in all, I highly recommend this book. Both thumbs up!

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