LEADER 03991nam 22006015 450 001 9910502636803321 005 20250504232001.0 010 $a9783030743611 010 $a3030743616 024 7 $a10.1007/978-3-030-74361-1 035 $a(CKB)4100000012037355 035 $a(MiAaPQ)EBC6735881 035 $a(Au-PeEL)EBL6735881 035 $a(OCoLC)1269616754 035 $a(PPN)258056673 035 $a(DE-He213)978-3-030-74361-1 035 $a(EXLCZ)994100000012037355 100 $a20210927d2021 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aPermutation Statistical Methods with R /$fby Kenneth J. Berry, Kenneth L. Kvamme, Janis E. Johnston, Paul W. Mielke, Jr 205 $a1st ed. 2021. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2021. 215 $a1 online resource (677 pages) 311 08$a9783030743604 311 08$a3030743608 320 $aIncludes bibliographical references and indexes. 327 $aPreface -- 1 Introduction -- 2 The R Programming Language -- 3 Permutation Statistical Methods -- 4 Central Tendency and Variability -- 5 One-sample Tests -- 6 Two-sample Tests -- 7 Matched-pairs Tests -- 8 Completely-randomized Designs -- 9 Randomized-blocks Designs -- 10 Correlation and Association -- 11 Chi-squared and Related Measures -- References -- Index. 330 $aThis book takes a unique approach to explaining permutation statistics by integrating permutation statistical methods with a wide range of classical statistical methods and associated R programs. It opens by comparing and contrasting two models of statistical inference: the classical population model espoused by J. Neyman and E.S. Pearson and the permutation model first introduced by R.A. Fisher and E.J.G. Pitman. Numerous comparisons of permutation and classical statistical methods are presented, supplemented with a variety of R scripts for ease of computation. The text follows the general outline of an introductory textbook in statistics with chapters on central tendency and variability, one-sample tests, two-sample tests, matched-pairs tests, completely-randomized analysis of variance, randomized-blocks analysis of variance, simple linear regression and correlation, and the analysis of goodness of fit and contingency. Unlike classical statistical methods, permutation statistical methods do not rely on theoretical distributions, avoid the usual assumptions of normality and homogeneity, depend only on the observed data, and do not require random sampling. The methods are relatively new in that it took modern computing power to make them available to those working in mainstream research. Designed for an audience with a limited statistical background, the book can easily serve as a textbook for undergraduate or graduate courses in statistics, psychology, economics, political science or biology. No statistical training beyond a first course in statistics is required, but some knowledge of, or some interest in, the R programming language is assumed. 606 $aStatistics 606 $aBiometry 606 $aSocial sciences$xStatistical methods 606 $aStatistical Theory and Methods 606 $aBiostatistics 606 $aStatistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy 615 0$aStatistics. 615 0$aBiometry. 615 0$aSocial sciences$xStatistical methods. 615 14$aStatistical Theory and Methods. 615 24$aBiostatistics. 615 24$aStatistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy. 676 $a519.5 700 $aBerry$b Kenneth J.$0148872 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910502636803321 996 $aPermutation statistical methods with R$92890881 997 $aUNINA