A Primer of Permutation Statistical Methods - Berry, Kenneth J.;Johnston, Janis E;Mielke, Jr., Paul W.
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The primary purpose of this textbook is to introduce the reader to a wide variety of elementary permutation statistical methods. Permutation methods are optimal for small data sets and non-random samples, and are free of distributional assumptions. The book follows the conventional structure of most introductory books on statistical methods, and features chapters on central tendency and variability, one-sample tests, two-sample tests, matched-pairs tests, one-way fully-randomized analysis of variance, one-way randomized-blocks analysis of variance, simple regression and correlation, and the…mehr

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Produktbeschreibung
The primary purpose of this textbook is to introduce the reader to a wide variety of elementary permutation statistical methods. Permutation methods are optimal for small data sets and non-random samples, and are free of distributional assumptions. The book follows the conventional structure of most introductory books on statistical methods, and features chapters on central tendency and variability, one-sample tests, two-sample tests, matched-pairs tests, one-way fully-randomized analysis of variance, one-way randomized-blocks analysis of variance, simple regression and correlation, and the analysis of contingency tables. In addition, it introduces and describes a comparatively new permutation-based, chance-corrected measure of effect size.
Because permutation tests and measures are distribution-free, do not assume normality, and do not rely on squared deviations among sample values, they are currently being applied in a wide variety of disciplines. This book presents permutation alternatives to existing classical statistics, and is intended as a textbook for undergraduate statistics courses or graduate courses in the natural, social, and physical sciences, while assuming only an elementary grasp of statistics.

Autorenporträt
Kenneth J. Berry earned his Ph.D. in Sociology at the University of Oregon and is Professor in the Department of Sociology at Colorado State University, Fort Collins, Colorado, USA. His research interests are in non-parametric and distribution-free statistical methods. Kenneth L. Kvamme earned his Ph.D. in Anthropology at the University of California, Santa Barbara and is Professor in the Department of Anthropology and Director of the Archeo-Imagining Laboratory at the University of Arkansas, Fayetteville, Arkansas, USA. His research interests include archeological computer applications, GIS, lithic technology, remote sensing, geophysical prospecting, and spatial analysis methods. Janis E. Johnston earned her Ph.D. in Sociology at Colorado State University and is a Senior Technical Advisor for the U.S. Government in Alexandria, Virginia, USA and a Faculty Affiliate in the Department of Sociology at Colorado State University, Fort Collins, Colorado,USA. Paul W. Mielke, Jr. earned his Ph.D. in Biostatistics at the University of Minnesota and was Emeritus Professor of Statistics at Colorado State University, Fort Collins, Colorado, USA, and Fellow of the American Statistical Association. His research interests were in multivariate statistics and permutation statistical methods. Paul Mielke passed away on 20 April 2019.