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Permutation Statistical Methods with R (Paperback, 1st ed. 2021)
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Permutation Statistical Methods with R (Paperback, 1st ed. 2021)
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This 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.
General
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