Abdelmonem Afifi, Abdelmonem Afifi, Susanne May, Susanne May, Robin Donatello, Virginia A. Clark, Virginia A. Clark

July 5, 2011
by Chapman and Hall/CRC

Textbook
- 537 Pages
- 75 B/W Illustrations

ISBN 9781439816806 - CAT# K10864

Series: Chapman & Hall/CRC Texts in Statistical Science

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- Offers a thorough and up-to-date presentation of multivariate statistical analysis
- Presents many examples to illustrate the geometric and graphical arguments
- Keeps the mathematics at a minimum level
- Focuses on applications to real-life problems
- Explains how R, S-PLUS, SAS, SPSS, Stata, and STATISTICA can be used for data analyses
- Includes problems at the end of each chapter
- Provides data sets and code for download at CRC Press Online and the book’s web page

*Solutions manual available for qualifying instructors*

This new version of the bestselling *Computer-Aided Multivariate Analysis* has been appropriately renamed to better characterize the nature of the book. Taking into account novel multivariate analyses as well as new options for many standard methods,** Practical Multivariate Analysis, Fifth Edition** shows readers how to perform multivariate statistical analyses and understand the results. For each of the techniques presented in this edition, the authors use the most recent software versions available and discuss the most modern ways of performing the analysis.

**New to the Fifth Edition**

- Chapter on regression of correlated outcomes resulting from clustered or longitudinal samples
- Reorganization of the chapter on data analysis preparation to reflect current software packages
- Use of R statistical software
- Updated and reorganized references and summary tables
- Additional end-of-chapter problems and data sets

The first part of the book provides examples of studies requiring multivariate analysis techniques; discusses characterizing data for analysis, computer programs, data entry, data management, data clean-up, missing values, and transformations; and presents a rough guide to assist in choosing the appropriate multivariate analysis. The second part examines outliers and diagnostics in simple linear regression and looks at how multiple linear regression is employed in practice and as a foundation for understanding a variety of concepts. The final part deals with the core of multivariate analysis, covering canonical correlation, discriminant, logistic regression, survival, principal components, factor, cluster, and log-linear analyses.

While the text focuses on the use of R, S-PLUS, SAS, SPSS, Stata, and STATISTICA, other software packages can also be used since the output of most standard statistical programs is explained. Data sets and code are available for download from the book’s web page and CRC Press Online.

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