Nonparametric Statistical Inference (Statistics: A Series of Textbooks and Monographs)

Proven Material for a Course on the Introduction to the Theory and/or on the Applications of Classical Nonparametric Methods

Since its first publication in 1971, Nonparametric Statistical Inference has been widely regarded as the source for learning about nonparametric statistics. The fifth edition carries on this tradition while thoroughly revising at least 50 percent of the material.

New to the Fifth Edition

• Updated and revised contents based on recent journal articles in the literature
• A new section in the chapter on goodness-of-fit tests
• A new chapter that offers practical guidance on how to choose among the various nonparametric procedures covered
• Additional problems and examples
• Improved computer figures

This classic, best-selling statistics book continues to cover the most commonly used nonparametric procedures. The authors carefully state the assumptions, develop the theory behind the procedures, and illustrate the techniques using realistic research examples from the social, behavioral, and life sciences. For most procedures, they present the tests of hypotheses, confidence interval estimation, sample size determination, power, and comparisons of other relevant procedures. The text also gives examples of computer applications based on Minitab, SAS, and StatXact and compares these examples with corresponding hand calculations. The appendix includes a collection of tables required for solving the data-oriented problems.

Nonparametric Statistical Inference, Fifth Edition provides in-depth yet accessible coverage of the theory and methods of nonparametric statistical inference procedures. It takes a practical approach that draws on scores of examples and problems and minimizes the theorem-proof format.

Jean Dickinson Gibbons was recently interviewed regarding her generous pledge to Virginia Tech.