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.
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:
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Handbook of Applied Multivariate Statistics and Mathematical Modeling
Manufacturer: Academic Press
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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.
Book Description
Multivariate designs were once the province of the very few exalted researchers who understood the underlying advanced mathematics. Today, through the sophistication of statistical software packages such as SPSS, virtually all graduate students across the social and behavioral sciences are exposed to the complex multivariate statistical techniques without having to learn the mathematical computations needed to acquire the data output.
These students - in psychology, education, political science, etc. - will never be statisticians and appropriately so, their preparation and coursework reflects less of an emphasis on the mathematical complexities of multivariate statistics and more on the analysis and the interpretation of the methods themselves and the actual data output.
This book provides full coverage of the wide range of multivariate topics in a conceptual rather than mathematical approach. The authors gear the text toward the needs, level of sophistication, and interest in multivariate methodology of students in these applied programs who need to focus on design and interpretation rather than the intricacies of specific computations.
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Coverage of the most widely used multivariate designs: multiple regression, exploratory factor analysis, MANOVA, and structural equation modeling.
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Integrated SPSS examples for hands-on learning from one large study (for consistency of application throughout the text).
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Examples of written results to enable students to learn how the results of these procedures are communicated.
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Practical application of the techniques using contemporary studies that will resonate with students.
Customer Reviews:
couldn't have finished my thesis without it.......2007-10-01
This is probably one of the most straightforward statistics books i have used. It got me through my MA's and now I am relying on it to finish my PhD dissertation. If you are looking for a quick answer to a review, or simply looking for a statistics book to buy, "BUY IT NOW."
I am a phD student in applied child development who does a lot of applied and educational research. This book has all the basic details and general ideas about statistics that most researchers would use, ranging from basic bivariate comparisons to advanced ideas such as factor analysis. It presents each topic in a very straightforward manner with simple terms and examples (mostly in psychology), and each chapter is accompanied by a chapter on how to implement the particular statistical procedure in SPSS. Unlike other books where they either present you with the mathematics of each statistical procedure, or books where they assumes you know what procedure to use and only present you with SPSS commands, this book presents both, though heavier on the latter.
This is particularly good for researchers like myself who need just enough information to choose which procedure to use (and not really needing to know all the mathematical proofs) and need step by step instructions to compute the statistics on SPSS (with screenshots, arrows pointing to what buttons to press, what to read, etc). However, please note that all the SPSS chapters, which include detailed screenshots, would probably make sense to people who are using SPSS 13.0 to SPSS 15.0 (12.0 would probably be fine, but I have never used anything before 12.0 so I can't comment on that). I imagine that version 16 would likely be the same, but being heavy on screenshots of a particular version of SPSS (windows version, nonetheless) means that this book would not last forever. Though, unless SPSS makes hugh interface changes in the future, anyone reading this book should be able to follow.
The biggest ++ of this book, from my perspective, is the way this book presents each example statistical problem (again, psychology heavy). It begins each concept chapter with an example research question, details why a particular statistical procedure is appropriate, tells you what to look for, assumptions underlying the statistics, basic ideas and basic algorithms of the statistics, and then what the statistics would show you. Then, the accompanying SPSS chapter follows with the SAME research problem, shows you how to find if the sample distribution satisfies assumptions in SPSS (with screenshots tell you which button to press!!!), tell you what numbers to look for (sometimes with assumption guidelines), what commands to use to run the statistical procedure(s), a detailed description of the output and what numbers to look for, and most importantly, an APA style example write up, with APA style charts and tables when appropriate.
You can tell that I LOVE THIS BOOK!~
However, I am not a statistician or a psychometrician. This book is not intended for people who are studying statistics or psychometrics. It does not provide detailed proofs, which is fine by me but many students would probably find this lacking. Further, it covers everything from data screening to MANOVA and Exploratory Factor Analysis well, but not anything beyond that (an introduction to confirmatory factor analysis using Amos is provided, but Amos, though related to SPSS, is not the most widely used CFA tool). Further, it presents only parametric data analysis techniques, which are common for most researchers. However, for my current research, I supplement this book with Pett's book on non-parametric statistics.
Get IT... click BUY IT NOW....
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.
Book Description
Description: Real-world problems and data sets are the backbone of this book, which provides a unique approach to the topic, integrating statistical methods, data analysis, and applications. Now extensively revised, the book includes new information about mixed effects models, applications of the MIXED procedure, regression diagnostics with the corresponding IML procedure code, and covariance structures. The authors' approach to the information will aid professors, researchers, and students in a variety of disciplines and industries. Extensive SAS code and the corresponding high-resolution output accompany sample problems, and clear explanations of SAS procedures are included. Emphasis is on correct interpretation of the output to draw meaningful conclusions. Featuring both the theoretical and the practical, topics covered include multivariate analysis of experimental data and repeated measures data, graphical representation of data including biplots, and multivariate regression. In addition, a quick introduction to the IML procedure with special reference to multivariate data is available in an appendix. SAS programs and output integrated with the text make it easy to read and follow the examples.
Customer Reviews:
Very Nice Introduction to Multivariate Analysis using SAS.......2000-02-11
The authors gave a short tutorial on the book at the Deming conference last year. I enjoyed the presentation and the book which is not expensive was discounted. I met both authors. They are experts in multivariate analysis having studied under C. R. Rao at Pittsburgh. The book provides lots of SAS code and does a good job of explaining how to set-up the analysis in SAS which would not be straightforward from the SAS manuals. The authors discuss diagnostics and with most concepts they explain them first for univariate problems and then show you how it is extended to the multivariate case. It is also one of the few books that deals with multivariate outliers. Outliers can be difficult to detect in regression and high dimensional multivariate data. I have some research experience in the area of outlier detection and can appreciate the way the authors deal with it.
Book Description
Demonstrates how to use confirmatory factor analysis--a model that allows researchers to specify the relationships among observed and latent variables on the basis of substantive considerations rather than mathematical convenience.
Book Description
Includes practical elements of matrix theory, continuous multivariate distributions and basic multivariate statistics in the normal distribution; regression and the analysis of variance; factor analysis and latent structure analysis; canonical correlations; stable portfolio analysis; classifications and discrimination models; control in the multivariate linear model; and structuring multivariate populations. 1982 edition.
Customer Reviews:
Applied Multivariate Analysis : Using Bayesian and Frequentist Methods of Inference. Second edition.......2006-02-06
Good quality & delivery, sold at reasonable price
Book Description
The idea of modelling systems using graph theory has its origin in several scientific areas: in statistical physics (the study of large particle systems), in genetics (studying inheritable properties of natural species), and in interactions in contingency tables. The use of graphical models in statistics has increased considerably over recent years and the theory has been greatly developed and extended. This book provides the first comprehensive and authoritative account of the theory of graphical models and is written by a leading expert in the field. It contains the fundamental graph theory required and a thorough study of Markov properties associated with various type of graphs. The statistical theory of log-linear and graphical models for contingency tables, covariance selection models, and graphical models with mixed discrete-continous variables in developed detail. Special topics, such as the application of graphical models to probabilistic expert systems, are described briefly, and appendices give details of the multivarate normal distribution and of the theory of regular exponential families. The author has recently been awarded the RSS Guy Medal in Silver 1996 for his innovative contributions to statistical theory and practice, and especially for his work on graphical models.
Book Description
Although clustering--the classification of objects into meaningful sets--is an important procedure in the social sciences today, cluster analysis as a multivariate statistical procedure is poorly understood by many social scientists. This volume is an introduction to cluster analysis for social scientists and students.
Customer Reviews:
clustering is often subjective.......2006-12-29
Aldenderfer provides a concise introduction to the various types of clustering methods typically used in the social sciences. If you are a researcher, you really should consult a more comprehensive text. But the current book at least offers brevity.
One key finding from the methods presented here is that the clusters are often subjective. That is, by tweaking various parameters in a given clustering method, you can end up with different clusters. More advanced statistical methods might be needed to discern which agglomerations are likely to be valid associations.
Also, the software described is very badly out of date. Inevitable when the book was written in 1984. Today, the latest packages can handle bigger data sets, and often have much friendly user interfaces.
Okay, but out of date and not very practical.......2006-05-18
I can imagine this book would have been very useful in 1984 when it was written, but with the myriad advances in statistical software, not to mention the field of cluster analysis itself, it isn't that much use today. If you are looking to understand and perform cluster analyses with something like SPSS, this book is not at all what you need. If you are looking for a very general understanding of cluster analysis as it was 22 years ago then this might be okay, but otherwise you should look elsewhere.
I feel a bit cheated having bought it on the basis of the other reviewer's glowing recommendation. As a colleague of mine says "An idiot learns from his mistakes, a clever man learns from other people's". Learn from this idiot's mistake and don't buy this book.
Excellent primer on Cluster Analysis.......2004-07-17
I have become a big fan of this little green book series. I belong to a very quantitatively oriented in-house think tank of a major West Coast financial service institution. As a very regular MBA, I often wonder what I am doing in such a group. These little green books have bailed me out several times and provided me the understanding on various esoteric advanced statistical methods. Thanks to these books I taught myself Logistic Regression, and Discriminant Analysis.
About two weeks ago, one of our best Russian mathematicians left our group. He had developed an expertise in Cluster Analysis. My boss assigned me to become his successor as a Cluster Analyst so to speak. If it were not for the green book series, I would have been in a state of panic. I quickly ordered the Cluster Analysis book. Studied it. And, now I am on my way to becoming a descent Cluster Analyst.
There are many eccentric features to Cluster Analysis. First, it is much less well grounded in mathematics and statistics than many other data analysis methods. For one thing, it was invented by biologists at first and further developed by many soft scientists of all kinds. The authors reflect this strange background of Cluster Analysis. One of them is a professor in anthropology, and the other a professor in clinical psychology. Somehow, these soft-scientists have a much greater need to classify their data (this is especially true of biologists) than pure mathematicians do. Second, there are tons of different ways to conduct Cluster Analysis. All have their benefits, and some have specific flaws. The authors do an excellent job at explaining and differentiating these different methods.
As usual [of the green book series], the book is very well written, and makes this complex methodology easily accessible. It is an excellent book to teach you Cluster Analysis. I strongly recommend it.
Cluster Analysis is a good thing to know. These days it is popping out everywhere. How do political campaign managers customize their political message through direct mail to specific voting groups? Cluster Analysis. Whether you are aware or not, we are all part of data clusters. How do college recruiters decide on which applicant to spend much recruiting energy? Cluster Analysis. Cluster Analysis is the answer to numerous unexpected questions.
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Statistical Inference
Bayesian Data Analysis, Second Edition (Texts in Statistical Science)
An Introduction to Multivariate Statistical Analysis (Wiley Series in Probability and Statistics)
Categorical Data Analysis (Wiley Series in Probability and Statistics)
Modern Applied Statistics with S
