Applied Statistics

This work has been thoughtfully designed so that it serves equally well as a reference for the practitioner and as a self-contained textbook for the advanced student.
* Rewritten to maintain clarity and brevity while expanding the coverage of previous editions.
* Changes to design-related topics include increased discussion of mixed models and random effects, greater emphasis on regression and data screening, and more use of graphs throughout.
* Includes both graded and challenging exercises.
* Liberal computer discussions now supplemented with SAS and SPSS. Preface.

1. Data Screening.

Problems.

References.

2. One-Way Analysis of Variance Design.

Problems.

References.

3. Estimation and Simultaneous Inference.

Problems.

References.

4. Hierarchical or Nested Design.

Problems.

References.

5. Two Crossed Factors: Fixed Effects and Equal Sample Sizes.

Problems.

References.

6 Randomized Complete Block Design.

Problems.

References.

7. Two Crossed Factors: Fixed Effects and Unequal Sample Sizes.

Problems.

References.

8. Crossed Factors: Mixed Models.

Problems.

References.

9. Repeated Measures Designs.

Problems.

References.

10. Linear Regression: Fixed X Model.

Problems.

References.

11. Linear Regression: Random X Model and Correlation.

Problem.

References.

12. Multiple Regression.

Problems.

References.

13. Multiple and Partial Correlation.

Problems.

References.

14. Miscellaneous Topics in Regression.

Problems.

References.

15. Analysis of Covariance.

Problems.

References.

16. Summaries, Extensions, and Communication.

References.

Appendix A.

Appendix B.

Index. "…beginning level graduate students in statistics will find this book very valuable." (Journal of Statistical Computation and Simulation, January 2006)

"…Overall this is an excellent book…I recommend this book to everyone…" (Statistical Methods in Medical Research, Vol. 14, 2005)

"…The book is an excellent material for describing the data as well as a useful textbook for students to understand, apply and to interpret the statistical methods." (Zentralbaltt MATH, May 2005)

"…it contains a wealth of useful up-to-date information and examples from the health sciences." (Technometrics, November 2004)

"The level of the book is also more consistently intermediate...a few more advanced topics are now excluded." (The American Statistician, August 2004)

RUTH M. MICKEY, PHD, is Professor of Statistics at the University of Vermont in Burlington.

OLIVE JEAN DUNN, PHD, is Professor Emeritus at the University of California, Los Angeles.

VIRGINIA A. CLARK, PHD, is also Professor Emeritus at the University of California, Los Angeles. Drs. Dunn and Clark are the authors of Basic Statistics: A Primer for Biomedical Sciences, Third Edition, published by Wiley.

A valuable new edition of a standard in the field of statistics

For the ever-increasing number of practitioners who must perform sophisticated statistical analyses on computers, Applied Statistics: Analysis of Variance and Regression has long been a standard reference and text. This new Third Edition has been thoroughly revised to provide a comprehensive and up-to-date combination of sound statistical methodology, practical advice on the application of this methodology, and interpretation of output from statistical programs.

Special features of this edition include:

• A highly readable text, with mathematical background kept at a low level
• Comprehensive treatment of each topic, from summarization of data to presentation of results
• Careful explanation of assumptions, how to check if the data meet them, and what to do if they don't
• A greater emphasis on regression, data screening, and confidence intervals
• In-depth discussion of design-related topics such as mixed models and random effects
• Graded and challenging exercises at the end of each chapter and on the Wiley ftp site
• Overviews of more advanced topics
• An extensively revised chapter on repeated measures designs

Eminently suitable for students and researchers who may not have a calculus background as well as for more advanced practitioners, this self-contained, comprehensive treatment of variance and regression analysis readies a new generation of researchers for the demands of this growing field.