Distribution-free statistical methods enable users to make statistical inferences with minimum assumptions about the population in question. They are widely used, especially in the areas of medical and psychological research.
This new edition is aimed at senior undergraduate and graduate level. It also includes a discussion of new techniques that have arisen as a result of improvements in statistical computing. Interest in estimation techniques has particularly grown, and this section of the book has been expanded accordingly. Finally, Distribution-Free Statistical Methods includes more examples with actual data sets appearing in the text.
Table of Contents
Basic Concepts in Distribution-Free Methods. One-Sample Location Problems. Miscellaneous One-Sample Problems. Two-Sample Problems. Straight Line Regression. Multiple Regression and General Linear Models. Bivariate Problems. Miscellaneous Complements. References. Index.
Johannes Maritz is professor in the Department of Statistics , University of Stellenbosch, South Africa.
"In summary, the book is both readable and informative. It probably covers more material than would normally be included in a standard course on distribution-free methods, but it would be a useful reference for any student following such a course."