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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.
- Sales Rank: #3732839 in Books
- Published on: 1995-04-01
- Original language: English
- Number of items: 1
- Dimensions: 9.00" h x 5.75" w x .75" l, .85 pounds
- Binding: Hardcover
- 256 pages
Review
"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." -The Statistician
From the Back Cover
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 will induce more examples with actual data sets appearing in the text.
Most helpful customer reviews
29 of 29 people found the following review helpful.
well written monograph
By Michael R. Chernick
This is a concise and well written book on distribution free methods, a subset of nonparametric techniques. Distribution free means that the distribution of the test statistic does not depend on the underlying sampling distribution when the null hypothesis holds. Usually this requires only minimal assumptions such as continuity of the population distribution function.
This series is well-known for its excellent short monographs that cover the important aspects on the topic but in an clear and concise manner. This text is certianly no exception to that characteristic.
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