Applied Statistics: Analysis of Variance and Regression

A self-contained, comprehensive treatment of variance and regression analysis suitable for students and researchers who may not have a calculus background. Models, tests, assumptions and confidence intervals allow readers to locate the material, know when to do the analysis, and correctly interpret results. New information in this edition, not found in other texts at this level, includes discussions of widely used computer program to perform the analysis, and a new chapter on repeated measures designs.

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.