PDF Ebook Multivariate Data Analysis (7th Edition), by Joseph F. Hair Jr, William C. Black, Barry J. Babin, Rolph E. Anderson
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Multivariate Data Analysis (7th Edition), by Joseph F. Hair Jr, William C. Black, Barry J. Babin, Rolph E. Anderson
PDF Ebook Multivariate Data Analysis (7th Edition), by Joseph F. Hair Jr, William C. Black, Barry J. Babin, Rolph E. Anderson
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For graduate and upper-level undergraduate marketing research courses.
For over 30 years, Multivariate Data Analysis has provided readers 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 readers how to understand and make use of the results of specific statistical techniques.
In this Seventh Edition, 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.
- Sales Rank: #373596 in Books
- Brand: Hair, Joseph F., Jr./ Black, William C./ Babin, Barry J./ Anderson, Rolph E.
- Published on: 2009-02-23
- Original language: English
- Number of items: 1
- Dimensions: 10.00" h x 1.60" w x 8.10" l, 3.45 pounds
- Binding: Paperback
- 816 pages
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.
About the Author
Dr. Joe Hair is Founder and Senior Scholar of the Doctoral Degree in Business Administration, Coles College, Kennesaw State University, USA. He previously held the Copeland Endowed Chair of Entrepreneurship and was Director, Entrepreneurship Institute, Ourso College of Business Administration, Louisiana State University. He has authored over 40 books, including "Multivariate Data Analysis", Prentice-Hall, 7th edition, 2010 (cited 22,000+ times); "Marketing", South-Western Publishing Company, 12th edition 2012; "Essentials of Business Research Methods", M.E. Sharpe, 2011; "Research Methods for Business", Wiley, 2007; and "Essentials of Marketing Research", McGraw-Hill/Irwin, 3rd edition 2013. He also has published numerous articles in scholarly journals such as the "Journal of Marketing Research", "Journal of Academy of Marketing Science", "Journal of Business/Chicago", "Journal of Advertising Research", "Journal of Business Research", "Journal of Long Range Planning", "Journal of Marketing Theory and Practice", "International Marketing Review", "Journal of Experimental Education", "Business Horizons", "Journal of Retailing", "Multivariate Behavioral Research", and others. He was recognized as the 2011 Academy of Marketing Science Marketing Educator of the year. He often presents seminars on research techniques, multivariate data analysis and marketing issues for organizations in Europe, Australia and other locations outside the U.S.
Barry J. Babin has authored over 70 research publications in some of the most prestigious research periodicals, including the JOURNAL OF MARKETING, THE JOURNAL OF CONSUMER RESEARCH, THE JOURNAL OF BUSINESS RESEARCH, THE JOURNAL OF RETAILING, PSYCHOLOGICAL REPORTS, PSYCHOLOGY AND MARKETING, and THE JOURNAL OF THE ACADEMY OF MARKETING SCIENCE, among others. Babin is currently Max P. Watson, Jr., Professor of Business and chair of the department of marketing and analysis at Louisiana Tech University. He has won numerous honors for his research, including the Louis K. Brandt Faculty Research Award from the University of Southern Mississippi (on three occasions), the 1996 Society for Marketing Advances (SMA) Steven J. Shaw Award, the 1997 Omerre Deserres Award for Outstanding Contributions to Retail and Service Environment Research, and the Academy of Marketing Science's Harold W. Berkman Distinguished Service Award. He is a former president of the Academy of Marketing Sciences and the Society of Marketing Advances, and he currently serves the marketing editor for the JOURNAL OF BUSINESS RESEARCH. Babin's research focuses on the effect of the service environment in creating value for employees and customers. His expertise is in building and understanding value that leads to long-lasting, mutually beneficial relationships with employees and customers. His primary teaching specialties involve consumers and service quality, marketing research, and creative problem solving. A frequent international lecturer, he has presented in Australia, South Korea, France, Germany, New Zealand, South Africa, Canada, Sweden, and the United Kingdom.
Most helpful customer reviews
11 of 12 people found the following review helpful.
THE BEST REFERENCE FOR PHD STUDENT.....YOU ALL SHOULD HAVE ONE...
By Azmizam Abdul Rashid
Multivariate data analysis is a central tool whenever several variables need to be considered at the same time. The present book explains a powerful and versatile way to analyse data tables, suitable also for researchers without formal training in statistics.
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.
8 of 9 people found the following review helpful.
The BEST stats book anywhere
By Stellagirl
This is my all time favorite stats book. It's very easy to read and yet is authoritative and comprehensive. The book is designed to promote a real understanding of the topic. Each chapter includes: glossary and quick overview at the beginning of the chapter, to orient the reader to the chapter; description of the theory; an application; example of programming code; summary. The writing style is clear and easy to follow. I first read this book in graduate school. I still turn to it as a refresher. If I could own only one multivariate book, this would be it.
5 of 5 people found the following review helpful.
Understanding complex statistics
By Jo Anne Carrick
This book was referred to me while I was writing the data analysis chapter of my dissertation. What a good find. I thought I understood multivariate analysis but this gives you practical and clear appliacations that make sense. A must reference if your work goes beyond the simple t-test.
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