- Paperback: 816 pages
- Publisher: Pearson; 7 edition (February 23, 2009)
- Language: English
- ISBN-10: 0138132631
- ISBN-13: 978-0138132637
- Product Dimensions: 8 x 1.5 x 9.6 inches
- Shipping Weight: 3.8 pounds (View shipping rates and policies)
- Average Customer Review: 58 customer reviews
- Amazon Best Sellers Rank: #220,598 in Books (See Top 100 in Books)
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Multivariate Data Analysis (7th Edition) 7th Edition
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From the Back Cover
KEY BENEFIT: For over 30 years, this text has provided students with the information they need to understand and apply multivariate data analysis. Hair, et. al provides an applications-oriented introduction to multivariate analysis for the non-statistician. By reducing heavy statistical research into fundamental concepts, the text explains to students how to understand and make use of the results of specific statistical techniques. In this seventh revision, the organization of the chapters has been greatly simplified. New chapters have been added on structural equations modeling, and all sections have been updated to reflect advances in technology, capability, and mathematical techniques.
Preparing For a MV Analysis; Dependence Techniques; Interdependence Techniques; Moving Beyond the Basic Techniques
MARKET: Statistics and statistical research can provide managers with invaluable data. This textbook teaches them the different kinds of analysis that can be done and how to apply the techniques in the workplace.
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Top customer reviews
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One the negative side, a number of caveats has to be mentioned however: 1. This most recent edition, unfortunately is worse than previous one. I tend to recommend older editions. The authors apparently have been trying to include a lot of material that users (i.e. instructors) of the book thought was missing. The structure of the book suffers from this added material. Another problem is that really high-level material gets mixed up with elementary statistics. A textbook simply cannot be everything for everyone. 2. The book is too big. Too much material has been added to this text. Furthermore, often too much time is spend discussing the examples. E.g., the illustration of discriminant and logit analysis alone takes over 40 pages.
Overall my rating of this book is not negative. If this edition of the book had been my first encounter with this text, I probably had been more enthousiastic. Now I tend to compare it with earlier editions, and must conclude the book has degraded. A next edition, in which a good editor goes through the text to enhance structure and get rid of superfluous material might easily solve the problems mentioned.
The analysis of multivariate data requires the extension of standard univariate statistical models and methods but also introduces new problems. Initial attention is given to Data Mining techniques such as summarising and displaying high dimensional data and to ways of reducing multivariate problems to more manageable univariate ones. This is followed by routine generalisations of standard distributions and statistical tests before consideration of new strategies for constructing hypothesis tests. Finally, problems specific to multivariate data such as discrimination and classification (use in medical diagnosis problems for example) are studied. Most of these methods can be implemented in standard computer packages.
This book shows that multivariate analysis are:
- Design for capability (also known as capability-based design)
- Inverse design, where any variable can be treated as an independent variable
- Analysis of Alternatives (A0A), the selection of concepts to fulfill a customer need
- Analysis of concepts with respect to changing scenarios
- Identification of critical design drivers and correlations across hierarchical levels.
Thank you to Joseph F. Hair, Ronald L. Tatham, Rolph E. Anderson, William Black for their excellent job..make my research so easy. Every Phd should have this book.
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.
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