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# Introductory Nonparametrics

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Language:  English
Introductory Nonparametrics gently introduces the reader to nonparametrics by describing some simple tests, some tests of the most frequently encountered experimental designs, and permutation testing.
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Introductory Nonparametrics gives a gentle introduction to nonparametric hypothesis testing. It describes some simple tests, such as the sign and runs tests, and the Kruskal-Wallis, Friedman and Durbin tests, tests of the most frequently encountered experimental designs, the completely randomised, randomised block and balanced incomplete block design respectively. Permutation testing, a fundamental nonparametric tool in its own rite, is introduced to calculate p-values for the tests discussed. A companion text gives detail of R code used throughout Introductory Nonparametrics.

1. A First Perspective on Nonparametric Testing
1. What are nonparametric methods?
2. The sign tests
3. Runs tests
4. The median test
5. The Wilcoxon tests
2. Nonparametric Testing in the Completely Randomised, Randomised Blocks and Balanced Incomplete Block Designs
1. Introduction and outline
2. The Kruskal-Wallis test
3. The Friedman test
4. The Durbin test
5. Relationships of Kruskal-Wallis, Friedman and Durbin tests with ANOVA F tests
6. Orthogonal contrasts: Page and umbrella tests
3. Permutation Testing
1. What is permutation testing and why it is important?
2. Nonparametric multifactor ANOVA when the levels of the factors are unordered
3. Revisiting some previous examples
This is an excellent introductory nonparametrics text. Explanations are clear, real data is often used and there are detailed solutions to the chapter exercises. Of particular note are the use of the free R software, many examples of the use of orthogonal polynomials, discussion of permutation tests and new nonparametric ANOVA. These 4 features are either not covered or covered only briefly in alternative texts. I note the author intends a companion advanced nonparametric text and perhaps in that advice on how to handle missing values and examples of data plots could be given. D.J. Best, PhD
This book is a must for a great introduction to non-parametric testing by one of the masters of the field. The book describes each of the key testing procedures with easy to follow examples.

J.C.W. Rayner

John Rayner is currently Honorary Professorial Fellow at the Centre for Statistical and Survey Methodology, School of Mathematics and Applied Statistics, University of Wollongong, NSW, Australia and Conjoint Professor of Statistics at the University of Newcastle in NSW, Australia. He served as Professor of Statistics and Head of Discipline at the University of Newcastle from 2006 to 2011 before retiring from full-time employment. Previously John worked full-time at the University of Otago in Dunedin, New Zealand from 1973 to 1992 and the University of Wollongong in NSW, Australia from 1992 to 2006.

John’s prime research interests are goodness of fit (assessing statistical models) and nonparametric statistics. He is the lead author of Smooth Tests of Goodness of Fit: Using R and A Contingency Table Approach to Nonparametric Testing. He has written over 150 research articles and books, many with his long-time friend and colleague John Best.

Now in his 71st year, John exercises moderately every day with the aim of participating in a weekly parkrun. These are timed 5km runs at venues all over the world. Last year he ran under 25 mins several times and, once, under 24 mins!