Applied Multivariate Statistical Analysis

Book Description

This market-leading book offers a readable introduction to the statistical analysis of multivariate observations. Its overarching goal is to provide readers with the knowledge necessary to make proper interpretations and select appropriate techniques for analyzing multivariate data. Chapter topics include aspects of multivariate analysis, matrix algebra and random vectors, sample geometry and random sampling, the multivariate normal distribution, inferences about a mean vector, comparisons of several multivariate means, multivariate linear regression models, principal components, factor analysis and inference for structured covariance matrices, canonical correlation analysis, and discrimination and classification. For experimental scientists in a variety of disciplines.

Customer Reviews:

Not as Applied as I Hoped.......2007-06-22

While this text covers a variety of multivariate techniques, the term "applied" is used loosely, in my opinion.

This is more a math-stat textbook than an applied statistics text.

I wish I had read the reviews (if they existed when I purchased the 4th edition), for I would have purchased a different text.

NOT a good intro to MVA.......2007-02-28

My prof used this book for multivariate statisitical analysis. I absolutely despise this book. For one, the answers to exercises come in a separate book, so the homework questions are worthless to me. The solutions will cost you an extra $100 or so if you buy that book. The index is extremely light, so if you want a quick reference to a topic - forget it. You'll need to skim through hundereds of pages that aren't referenced in the index. Important topics are illustrated in 1 example usually, and the reader is often left to guess how such problems could be adapted to different situations than what is illustrated.

Book Contents.......2006-03-10

The "search inside this book" feature was not available when this review was posted. Hope it helps.

CONTENTS

I. GETTING STARTED.
1. Aspects of Multivariate Analysis.
2. Matrix Algebra and Random Vectors.
3. Sample Geometry and Random Sampling.
4. The Multivariate Normal Distribution.
II. INFERENCES ABOUT MULTIVARIATE MEANS AND LINEAR MODELS.
5. Inferences About a Mean Vector.
6. Comparisons of Several Multivariate Means.
7. Multivariate Linear Regression Models.
III. ANALYSIS OF A COVARIANCE STRUCTURE.
8. Principal Components.
9. Factor Analysis and Inference for Structured Covariance Matrices.
10. Canonical Correlation Analysis
IV. CLASSIFICATION AND GROUPING TECHNIQUES.
11. Discrimination and Classification.
12. Clustering, Distance Methods and Ordination.
Appendix.
Data Index.
Subject Index.

I'm also a dullard, like the other reviewers here..........2006-02-10

I'm also a dullard like the other people reviewing the book on this sight. I'm not Greek, so I've never seen a capital sigma or other squiggles - frankly I didn't know that these squiggles existed!! I was looking for a statistics book that described statistics in a wooly, general, hand waving sort of way, just the way that I b@##%&i! my boss in meetings when statistics arise. Unfortunatley, this was not the book for me to learn this technique from. I don't know - I might have to finish my study of arithmetic (including long division), and then algebra, and then squiggles before I try again with this book. If you're a dullard like me, then keep away from this book - on the other hand it's an excellent book on applied statistical methods.

Yipes!.......2006-02-01

I used this text in one class for a PhD program that is now complete. Oh Boy! This is probably a good text if you have a very solid mathematics background, but it is totally incomprehensible to a mathematics greenhorn. I spent hours and hours trying to decipher the mathematical codes and formulas. Some of the symbols were figures I didn't even know existed.

Applied? Nope - totally theoretical from stem to stern.

Generalized Linear Models, Second Edition (Monographs on Statistics and Applied Probability)

Average customer rating:

As promised, on time
first great treatment of generalized linear models
Very comprehensive, very helpful.
One of the best books on modelling

Generalized Linear Models, Second Edition (Monographs on Statistics and Applied Probability)
P. McCullagh , and John A. Nelder
Manufacturer: Chapman & Hall/CRC
ProductGroup: Book
Binding: Hardcover

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Bootstrap Methods and Their Application (Cambridge Series in Statistical and Probabilistic Mathematics , No 1)

ASIN: 0412317605

Book Description

The success of the first edition of Generalized Linear Models led to the updated Second Edition, which continues to provide a definitive unified, treatment of methods for the analysis of diverse types of data. Today, it remains popular for its clarity, richness of content and direct relevance to agricultural, biological, health, engineering, and other applications. The authors focus on examining the way a response variable depends on a combination of explanatory variables, treatment, and classification variables. They give particular emphasis to the important case where the dependence occurs through some unknown, linear combination of the explanatory variables. The Second Edition includes topics added to the core of the first edition, including conditional and marginal likelihood methods, estimating equations, and models for dispersion effects and components of dispersion. The discussion of other topics-log-linear and related models, log odds-ratio regression models, multinomial response models, inverse linear and related models, quasi-likelihood functions, and model checking-was expanded and incorporates significant revisions. Comprehension of the material requires simply a knowledge of matrix theory and the basic ideas of probability theory, but for the most part, the book is self-contained. Therefore, with its worked examples, plentiful exercises, and topics of direct use to researchers in many disciplines, Generalized Linear Models serves as ideal text, self-study guide, and reference.

Customer Reviews:

As promised, on time.......2006-03-21

I got this book in time and in perfect condition. Prompt delivery!!!

first great treatment of generalized linear models.......2000-08-09

Nelder and Wedderburn wrote the seminal paper on generalized linear models in the 1970s. Since then John Nelder has pioneered the research and software development of the methods. This is the first of several excellent texts on generalized linear models. It illustrates how through the use of a link function many classical statistical models can be unified into one general form of model. This unification is helpful both theoretically and computationally. Various applications are presented in a clear manner.

Very comprehensive, very helpful........2000-04-02

The first edition is already a well-known text and reference, this expanded version is even better. Very comprehensive and very helpful.

One of the best books on modelling.......2000-04-01

This is an important book. It is a mature, deep introduction to generalized linear models.

General linear models extend multiple linear models to include cases in which the distribution of the dependent variable is part of the exponential family and the expected value of the dependent variable is a function of the linear predictor. Besides the normal (Gaussian) distribution, the binomial distribution, the Poisson distribution and the Gamma distribution, are just some of the exponential family members most frequently encountered in the scientific literature. Using appropriate functions to join the dependent variable to the linear predictor many classic models of applied statistics are included in the broad frame of generalized linear models: "logistic regression", log-linear models, Cox's proportional hazards models are just some of them.

Further extensions to the "base" family of generalized linear models, such as those based on the use of quasi-likelihood functions, and models in which both the expected value and the dispersion are function of a linear predictor, are well presented in the book.

Examples, and exercises, introduce many non-banal, useful, designs.

Book Description

With meta-analysis methods playing a crucial role in health research in recent years, this important and clearly-written book provides a much-needed survey of the field.
Meta-analysis provides a framework for combining the results of several clinical trials and drawing inferences about the effectiveness of medical treatments. The move towards evidence-based health care and practice is underpinned by the use of meta-analysis. This book:
* Provides a thorough criticism and an up-to-date survey of meta-analysis methods
* Emphasises the practical approach, and illustrates the methods by numerous examples
* Describes the use of Bayesian methods in meta-analysis
* Includes discussion of appropriate software for each analysis
* Includes numerous references to more advanced treatment of specialist topics
* Refers to software code used in the examples available on the authors' Web site
Practising statisticians, statistically-minded clinicians and health research professionals will benefit greatly from the clear presentation and numerous examples. Medical researchers will grasp the basic principles of meta-analysis, and learn how to apply the various methods.

Customer Reviews:

A personal review.......2007-05-13

This book has two big advantages: In my personal view it is written well for someone who has at least some basic statistical understanding and for me there are quite enough practical examples and illustrations of the chosen methodology. The second advantage in my opinion is its wealth of references at the end of every chapter.

Overall it covers almost all of the relevant methods and thus can be considered as a reference for the experienced statistician and as an easily comprehensible introduction to the beginner.

I think to recommend this book warmly to any reader who does not need the theoretical foundations of probability in meta-analysis.

Applied Longitudinal Analysis (Wiley Series in Probability and Statistics)

Average customer rating:

My favorite introductory statistics book
It's very good introduction textbook
Perfect for the applied researcher

Applied Longitudinal Analysis (Wiley Series in Probability and Statistics)
Garrett M. Fitzmaurice , Nan M. Laird , and James H. Ware
Manufacturer: Wiley-Interscience
ProductGroup: Book
Binding: Hardcover

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Longitudinal Data Analysis (Wiley Series in Probability and Statistics)

ASIN: 0471214876

Book Description

A rigorous, systematic presentation of modern longitudinal analysis
Longitudinal studies, employing repeated measurement of subjects over time, play a prominent role in the health and medical sciences as well as in pharmaceutical studies. An important strategy in modern clinical research, they provide valuable insights into both the development and persistence of disease and those factors that can alter the course of disease development.
Written at a technical level suitable for researchers and graduate students, Applied Longitudinal Analysis provides a rigorous and comprehensive description of modern methods for analyzing longitudinal data. Focusing on General Linear and Mixed Effects Models for continuous responses, and extensions of Generalized Linear Models for discrete responses, the authors discuss in detail the relationships among these different models, including their underlying assumptions and relative merits. The book features:
* A focus on practical applications, utilizing a wide range of examples drawn from real-world studies
* Coverage of modern methods of regression analysis for correlated data
* Analyses utilizing SAS(r)
* Multiple exercises and "homework" problems for review
An accompanying Web site features twenty-five real data sets used throughout the text, in addition to programming statements and selected computer output for the examples.

Customer Reviews:

My favorite introductory statistics book.......2006-05-13

The authors have done a masterful job. They've created a book that is accessable to those without a strong mathematics background, but still interesting to those with such a background. The scope is broad, yet one does not feel "shortchanged" on any topic covered. They cover both linear and generalized linear models, with and without mixed effects. Part IV contains what the authors call advanced topics such as missing data and multilevel models and their lucidity, given such brief treatment is astonishing.

It's very good introduction textbook.......2006-04-26

Tis book is very easy to read and understand. If you have the basic idea about the linear algebra. I recommend this book for people who want to self-teach. Since you can catch the concepts quickly.

Perfect for the applied researcher.......2005-07-01

If you need to do longitudinal analyses, and have a moderate mathermatical background, this is a book you should get, particularly if you use SAS. The authors present a wide variety of models clearly, describe their advantages and disadvantages, and illustrate how to use SAS to fit them. They keep the technical level modest (a little use of matrix algebra, but no calculus; not in theorem-proof style) while not sacrificing needed detail. In addition, they provide, at the end of each chapter, two sets of references: One at a similar level to this book, and one with more advanced material for those who wish (and are able) to explore it.

Multivariate Data Analysis (5th Edition)

Average customer rating:

Best general Multivariate stats book
Probably the best advanced stats book ever written...GOD bless the authors!
Sure it's good, and a good price by the pund too!
Good for a second stats course & reference
simple but great!!

Multivariate Data Analysis (5th Edition)
Joseph F. Hair , Ronald L. Tatham , Rolph E. Anderson , and William Black
Manufacturer: Prentice Hall
ProductGroup: Book
Binding: Hardcover

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ASIN: 0138948585

Book Description

Well-suited for the non-statistician, this applications-oriented introduction to multivariate analysis focuses on the fundamental concepts that affect the use of specific techniques rather than the mathematical derivation of the technique. Provides an overview of several techniques and approaches that are available to analysts today — e.g., data warehousing and data mining, neural networks and resampling/bootstrapping. Chapters are organized to provide a practical, logical progression of the phases of analysis and to group similar types of techniques applicable to most situations.

Customer Reviews:

Best general Multivariate stats book.......2007-06-07
This is without question THE BEST introduction to Multivariate Statistics book currently available. It is designed for the user of the techniques, not someone who wants to examine the math underlying the techniques. I have created a collection of the various editions of this book and I have all of them going from the 1st edition to the current one. Personally I really likely the 2nd and 3rd editions but the current one is also very good. Whether you are interested in Exploratory Factor Analysis, Multiple Regression Analysis, Discriminant Analysis [I think that there should have been more on classification analysis in this section], Logistic regression, multivariate analysis of variance, conjoint analysis, cluster analysis, multidimensional scaling, Confirmatory Factor Analysis or Structural Equation Modeling, this book provides a good broad overview as to how to use and interpret the techniques. The key terms for each technique are defined clearly technique by technique. Having taught faculty how to teach multivariate statistics this is the book that I chose to use. It is important to remember that it is BROAD overview and if you are going to do serious analyses that you'd likely want to get additional books about the specific technique or techniques that you are going to use.
Probably the best advanced stats book ever written...GOD bless the authors!.......2007-05-12
Over the course of my undergrad, grad, and post grad, I have read a variety of statistics books. Without a doubt, Hair's Multivariate Data Analysis is THE BEST book of them all. Here is a brief outline of the awesome features of the book:

1. The book itself is very well organized - chapter order and the order within each chapter helps the reader in knowing what is coming next and provides a sense of direction. I think this is a very important feature for any book to have especially when the topics are complex and are discussed over 800 odd pages.

2. The HBAT data set that comes along with the book (or that is provided by the instructor of the course depending on the version of the book you purchase) is really a very good resource. All multivariate techniques in the book can be carried out using this data set. The data set is clearly explained at the end of the first chapter.

3. Tables of examples, the 'Rules of thumb" after each important concept discussion prove invaluable. This is akin to the managerial implication written at the end of lenghty academic articles. This is almost like saying - Here is the deal folks.....Much precise than the summary section, in bullet points, these rules of thumb acts as quick referece that captures the content of the discussion.

4. From chapter 4 onwards till the very end of the book, each chapter is divided into two halves - the first half is the concept dicsussion - in detail, with examples and in very simple and understandable language. The second half is the illustration of the discussed concept through a very elaborate example using the HBAT data set. This arrangement not only helps the reader in better understanding the complex concepts, but also allows the reader to get their hands dirty by actually working out.

5. Keywords at the begining of each chapter provides a list of all the 'jargon' that would be used in that chapter. This list provides a detail definition of each term. Many times while reading the chapter, you would come across a confusing term and in those times the keyword list can prove invaluable.

All in all, this is an invaluable book. If you are a taking stats and you have not read this book, you are missing something. In spite of all the above great things, the best feature of this book is the writing style. I have not come across a book that explains concepts is such easy to understand language but at the same time not over simplifying the subject matter.
My advanced stats became enjoyable because of this book. Really may GOD bless these authors for writing this book!!
Sure it's good, and a good price by the pund too!.......2005-10-26
A pretty good overview and a lot of in-depth material on Multivariate data analysis. Not quite a bed time read though.

I recommend this book as part of your analytical library.

If you liked this book, another good book on multivariate data analysis you may want to check out as well is Sharma, S.; Applied Multivariate Techniques, New York: John Wiley & Sons, Inc., 1996.

If you want something easier to read/more practical, and you prefer SPSS over SAS you may want to check out either `Discovering Statistics using SPSS for Windows' by Andy Field, or probably even better/simpler `SPSS Survival Manual' by Pallant.

Tom Anderson
Anderson Analytics, LLC
(...)
Good for a second stats course & reference.......2005-08-03
We used this book for our Stats 2 course in grad school, and although our professor was good enough to eclipse Hair, et al. (he had written his own Stats text), I am most pleased at how much mileage I have drawn from Hair, et al. in the years since I took the course. In my subsequent career, reviewers have often sent me back to Hair, et al. when they have questions about something I'm doing with a data analysis, so it has become an invaluable reference for that reason alone.

The chapters on structural equation modeling, MANOVA, and factor analysis are particularly useful and well-written. I recommend this book without reservations for graduate students and others who work with advanced statistics as part of their daily work.
simple but great!!.......2005-06-02
I used this book as a reference on the topics while I was working on a research project at the university. Because the book does not use complex mathematics to explain the multivariate statistics, it is easy for social sciences students to understand. Each chapter starts with an overview, step-by step procedures and ends with an example from the data set that is used throughout the book.

In addition to the common topics in multivariate, the book also includes the new analysis techniques as CHAID, neural network, and data mining.

Average customer rating:

Poorly executed approach

Not for the light hearted...

Pretty Satisfied

A Gem! Insider's guide to software results & what to avoid.

Applied Multivariate Statistics for the Social Sciences (Applied Multivariate STATS)
James P. Stevens
Manufacturer: Lawrence Erlbaum
ProductGroup: Book
Binding: Paperback

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ASIN: 0805837779

Book Description

This best-selling text is written for those who use, rather than develop, advanced statistical methods. Dr. Stevens focuses on a conceptual understanding of the material rather than proving results. Helpful narrative and numerous examples enhance understanding, and a chapter on matrix algebra serves as a review. Printouts from SPSS and SAS with annotations indicate what the numbers mean and encourage interpretation of the results. In addition to demonstrating how to use the packages effectively, the author stresses the importance of checking the data, assessing the assumptions, and ensuring adequate sample size (by providing guidelines) so that the results can be generalized. The new edition features a CD-ROM with the data sets and many new exercises. Ideal for courses on advanced or multivariate statistics found in psychology, education, and business departments, the book also appeals to practicing researchers with little or no training in multivariate methods. Prerequisites include a course on factorial analysis of variance. It does not assume a working knowledge of matrix algebra.

Customer Reviews:

Poorly executed approach.......2007-07-20
Book might be good as a class text, but it is not at all suited for self-study. The SAS how to description is marginal at best. A better separation between SAS and SPSS description would have been helpful.
Not for the light hearted..........2007-06-02
This text is not meant to be an introduction to multivariate statistics so please don't purchase it if you need to warm up to the more complicated statistical methods.
Pretty Satisfied.......2007-03-08
Clear and understandable explanations but not as in-depth in some areas of statistics. In general, good book to have for psychmetric analyses.
A Gem! Insider's guide to software results & what to avoid........1999-06-20
Almost everything you need to know about how to input and read software results from someone who has actually analyzed real ( not simplified class room problems.) Very comprehensive coverage of large field. Most important, Stevens gives an insider's guide on what to avoid, what to disbelieve, and what is valid in this thicket of overlapping techniques. Great intro and guide to use of Statistical Power analysis, which is the linch-pin to planning experiments and obtaining valid results. Marvelous, and usable tables not readily obtainable elswhere that actually solve complicated problems (e.g. intra-class correlation the implicit reduction of significance.) The tables allow you to get quick results so you can short-circuit your cryptic and overweight software for basic problems. My edition, especially the tables are nearly worn-out from overuse.

Average customer rating:

A good introduction to multivariate statistics

grad students

Multivariate Statistics for Wildlife and Ecology Research
Kevin McGarigal , Sam Cushman , and Susan Stafford
Manufacturer: Springer
ProductGroup: Book
Binding: Paperback

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Accessories:

Stable Isotope Ecology

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Linking Restoration and Ecological Succession (Springer Series on Environmental Management)

ASIN: 0387986421

Book Description
Wildlife researchers and ecologists make widespread use of multivariate statistics in their studies. With its focus on the practical application of the techniques of multivariate statistics, this book shapes the powerful tools of statistics for the specific needs of ecologists and makes statistics more applicable to their course of study. Multivariate Statistics for Wildlife and Ecology Research gives the reader a solid conceptual understanding of the role of multivariate statistics in ecological applications and the relationships among various techniques, while avoiding detailed mathematics and underlying theory. More important, the reader will gain insight into the type of research questions best handled by each technique and the important considerations in applying each one. Whether used as a textbook for specialized courses or as a supplement to general statistics texts, the book emphasizes those techniques that students of ecology and natural resources most need to understand and employ in their research. Detailed examples use real wildlife data sets analyzed using the SAS statistical software program. The book is specifically targeted for upper-division and graduate students in wildlife biology, forestry, and ecology, and for professional wildlife scientists and natural resource managers, but it will be valuable to researchers in any of the biological sciences. Kevin McGarigal is Assistant Professor and Sam Cushman is a doctoral candidate in the Department of Forestry and Wildlife Management at the University of Massachusetts. Susan Stafford is Head of the Forest Science Department at Colorado State University.
Customer Reviews:

A good introduction to multivariate statistics.......2007-01-26
This book is fairly easy to understand, even with little knowledge of multivariate statistics. The author uses specific examples relevant to ecological fields and does not focus on theory (which is a rarity in statistical manuals). It is, however, starting to get a bit outdated with some of the techniques gaining favor in the literature recently.
grad students.......2002-04-01
I am an ecology grad student and I have returned to this text again and again.

Average customer rating:
Handbook of Applied Multivariate Statistics and Mathematical Modeling

Manufacturer: Academic Press
ProductGroup: Book
Binding: Hardcover

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ASIN: 0126913609

Book Description
Multivariate statistics and mathematical models provide flexible and powerful tools essential in most disciplines. Nevertheless, many practicing researchers lack an adequate knowledge of these techniques, or did once know the techniques, but have not been able to keep abreast of new developments. The Handbook of Applied Multivariate Statistics and Mathematical Modeling explains the appropriate uses of multivariate procedures and mathematical modeling techniques, and prescribe practices that enable applied researchers to use these procedures effectively without needing to concern themselves with the mathematical basis. The Handbook emphasizes using models and statistics as tools. The objective of the book is to inform readers about which tool to use to accomplish which task. Each chapter begins with a discussion of what kinds of questions a particular technique can and cannot answer. As multivariate statistics and modeling techniques are useful across disciplines, these examples include issues of concern in biological and social sciences as well as the humanities.

Average customer rating:

A nice book

Analysis of Incomplete Multivariate Data (Monographs on Statistics & Applied Probability)
J.L. Schafer
Manufacturer: Chapman & Hall/CRC
ProductGroup: Book
Binding: Hardcover

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ASIN: 0412040611

Book Description

The last two decades have seen enormous developments in statistical methods for incomplete data. The EM algorithm and its extensions, multiple imputation, and Markov Chain Monte Carlo provide a set of flexible and reliable tools from inference in large classes of missing-data problems. Yet, in practical terms, those developments have had surprisingly little impact on the way most data analysts handle missing values on a routine basis. Analysis of Incomplete Multivariate Data helps bridge the gap between theory and practice, making these missing-data tools accessible to a broad audience. It presents a unified, Bayesian approach to the analysis of incomplete multivariate data, covering datasets in which the variables are continuous, categorical, or both. The focus is applied, where necessary, to help readers thoroughly understand the statistical properties of those methods, and the behavior of the accompanying algorithms. All techniques are illustrated with real data examples, with extended discussion and practical advice. All of the algorithms described in this book have been implemented by the author for general use in the statistical languages S and S Plus. The software is available free of charge on the Internet.

Customer Reviews:

A nice book.......2006-03-23
The ideas are presented neatly. It is a good introduction for those who step into imcomplete data for the first time.

Average customer rating:
Regression Diagnostics: Identifying Influential Data and Sources of Collinearity (Wiley Series in Probability and Statistics)
David A. Belsley , Edwin Kuh , and Roy E. Welsch
Manufacturer: Wiley-Interscience
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Binding: Hardcover

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Applied Regression Analysis, Includes disk (Wiley Series in Probability and Statistics)

ASIN: 0471058564

Book Description
Provides practicing statisticians and econometricians with new tools for assessing quality and reliability of regression estimates. Diagnostic techniques are developed that aid in the systematic location of data points that are unusual or inordinately influential, and measure the presence and intensity of collinear relations among the regression data and help to identify variables involved in each and pinpoint estimated coefficients potentially most adversely affected. Emphasizes diagnostics and includes suggestions for remedial action.
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Provides practicing statisticians and econometricians with new tools for assessing quality and reliability of regression estimates. Diagnostic techniques are developed that aid in the systematic location of data points that are unusual or inordinately influential, and measure the presence and intensity of collinear relations among the regression data and help to identify variables involved in each and pinpoint estimated coefficients potentially most adversely affected. Emphasizes diagnostics and includes suggestions for remedial action.
Books:

Applied Multivariate Statistical Analysis

Applied Numerical Methods with MATLAB for Engineers and Scientists

Applied Numerical Methods with MATLAB for Engineering and Science w/ Engineering Subscription Card

Basic Technical Mathematics (8th Edition)

Bezier and B-Spline Techniques

Business Dynamics: Systems Thinking and Modeling for a Complex World with CD-ROM

Business Dynamics: Systems Thinking and Modeling for a Complex World with CD-ROM

Calculus and Analytic Geometry

Calculus with Applications, Brief Version (8th Edition) (Lial/Greenwell/Ritchey Series)

Capitalism: The Unknown Ideal

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