Distribution-free statistical methods
- 255 Pages
- 4.59 MB
- 5151 Downloads
Chapman & Hall , London, New York
Statistical hypothesis testing., Fix-point estimation., Nonparametric statis
|Series||Monographs on statistics and applied probability ;, 17|
|LC Classifications||QA277 .M36 1995|
|The Physical Object|
|Pagination||xii, 255 p. :|
|LC Control Number||95068499|
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2) Use distribution-free methods in studies where the response variable is believed to have a moderately or highly non-normal distribution where the traditional parameters (e.g., mean, standard deviation, Pearson correlation)File Size: KB.
Distribution-free statistical Distribution-free statistical methods book enable users to make statistical inferences with minimum assumptions about the population in question.
They are widely used, especially Distribution-free statistical methods book the areas of medical and psychological new edition is aimed at senior undergraduate and graduate level.
It al. Finally, Distribution-free Statistical Methods will induce more examples with actual data sets appearing in the text. About the Author Johannes Maritz is a professor in the Department of Statistics, University of Stellenbosch, South by: A really excellent book. Although written inthe author knows how to present the material.
Details Distribution-free statistical methods PDF
For example, the layout for a test: Section "Number of Runs Up and Down - as a test of randomness" is as follows: Rationale (one paragraph); Null Hypothesis (two short paragraphs); Assumptions (three sentences); Efficiency (again, short explanation); Tables (reference to 5/5(2).
Author by: J.S. Maritz Languange: en Publisher by: CRC Press Format Available: PDF, ePub, Mobi Total Read: 24 Total Download: File Size: 50,5 Mb Description: Distribution-free statistical methods enable users to make statistical inferences with minimum assumptions about the population in are widely used, especially in the areas of medical and.
"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.
distribution free methods, which do not rely on assumptions that the data are drawn from a given probability distribution.
As such it is the opposite of parametric statistics. It includes non-parametric statistical models, inference and statistical tests.
Description Distribution-free statistical methods FB2
distribution free methods, which do not rely on assumptions that the data are drawn from a given parametric family of probability distributions. As such it is the opposite of parametric statistics. nonparametric statistics (a statistic is defined to be a function. 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 new edition is aimed at. Open Library is an open, editable library catalog, building towards a web page for every book ever published.
Distribution-free statistical methods by J. Maritz,Chapman and Hall edition, in English. The EM Algorithm and Related Statistical Models, edited by Michiko Watanabe and Kazunori Yamaguchi Multivariate Statistical Analysis, Second Edition, Revised and Expanded, Narayan C.
Giri Computational Methods in Statistics and Econometrics, Hisashi Tanizaki Applied Sequential Methodologies: Real-World Examples with Data Analysis, edited by Nitis. Book Selection; Published: 01 July ; Distribution-Free Statistical Methods.
Chris Beaumont. If you are looking for a book to learn and apply statistical methods, this is a great one. I think the author could consider revising the title of the book to reflect the above, as it is more than just an introduction to statistics, may be include the word such as practical guide.
Content Accuracy rating: 5 The contents of the book seems accurate. The purpose of this book is to provide a general guide to statistical methods used in the pharmaceutical industry, and to illustrate how to use S-PLUS to implement these methods.
Statistical Methods of Discrimination and Classification: Advances in Theory and Applications is a collection of papers that tackles the multivariate problems of discriminating and classifying subjects into exclusive population.
The book presents 13 papers that cover that advancement in the statistical procedure of discriminating and classifying. Distribution-Free Methods for Statistical Process Monitoring and Control Koutras, M. (Ed), Triantafyllou, I.
(Ed) () This book explores nonparametric statistical process control. Statistical Approaches in Excellent Research Methods is a most clearly written modern and practical book with simple, direct, easy-to-understand and highly motivating information.
It is a fresh approach to the teaching, learning and application of statistics as an exciting field that has very central and robust place in research in particular. The chapter also shows how to obtain distribution‐free two‐sided confidence intervals and one‐sided confidence bounds for a distribution quantile.
It provides some initial comments about coverage probabilities, and presents methods and examples showing how to. Statistical methods that do not rely on the assumption of a known population probability distribution function for their validity are called Distribution-free Statistical Methods (also called.
William Q. Meeker is Professor of Statistics and Distinguished Professor of Liberal Arts and Sciences at Iowa State University. He is co-author of Statistical Methods for Reliability Data (Wiley, ) and of numerous publications in the engineering and statistical literature and has won many awards for his research.
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Gerald J. Hahn served for 46 years as applied statistician and manager of an. Praise for the Second Edition “This book should be an essential part of the personal library of every practicing statistician.”—Technometrics Thoroughly revised and updated, the new edition of Nonparametric Statistical Methods includes additional modern topics and procedures, more practical data sets, and new problems from real-life situations.
The book continues to emphasize the.
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