Buy Evidence-Based Statistics: An Introduction to the Evidential Approach – from Likelihood Principle to Statistical Practice
1st Edition PDF ebook by author Peter M. B. Cahusac – published by John Wiley & Sons P&T in 2021 and save up to 80% compared to the print version of this textbook. With PDF version of this textbook, not only save you money, you can also highlight, add text, underline add post-it notes, bookmarks to pages, instantly search for the major terms or chapter titles, etc.
You can search our site for other versions of the Evidence-Based Statistics: An Introduction to the Evidential Approach – from Likelihood Principle to Statistical Practice
1st Edition PDF ebook. You can also search for others PDF ebooks from publisher John Wiley & Sons P&T, as well as from your favorite authors. We have thousands of online textbooks and course materials (mostly in PDF) that you can download immediately after purchase.
Note: e-textBooks do not come with access codes, CDs/DVDs, workbooks, and other supplemental items.
eBook Details:
Full title: Evidence-Based Statistics: An Introduction to the Evidential Approach – from Likelihood Principle to Statistical Practice
1st Edition
Edition: 1st
Copyright year: 2021
Publisher: John Wiley & Sons P&T
Author: Peter M. B. Cahusac
ISBN: 9781119549802, 9781119549826
Format: PDF
Description of Evidence-Based Statistics: An Introduction to the Evidential Approach – from Likelihood Principle to Statistical Practice
1st Edition:
Evidence-Based Statistics: An Introduction to the Evidential Approach – from Likelihood Principle to Statistical Practice provides readers with a comprehensive and thorough guide to the evidential approach in statistics. The approach uses likelihood ratios, rather than the probabilities used by other statistical inference approaches. The evidential approach is conceptually easier to grasp, and the calculations more straightforward to perform. This book explains how to express data in terms of the strength of statistical evidence for competing hypotheses. The evidential approach is currently underused, despite its mathematical precision and statistical validity. Evidence-Based Statistics is an accessible and practical text filled with examples, illustrations and exercises. Additionally, the companion website complements and expands on the information contained in the book. While the evidential approach is unlikely to replace probability-based methods of statistical inference, it provides a useful addition to any statistician’s “bag of tricks.” In this book: It explains how to calculate statistical evidence for commonly used analyses, in a step-by-step fashion Analyses include: t tests, ANOVA (one-way, factorial, between- and within-participants, mixed), categorical analyses (binomial, Poisson, McNemar, rate ratio, odds ratio, data that’s ‘too good to be true’, multi-way tables), correlation, regression and nonparametric analyses (one sample, related samples, independent samples, multiple independent samples, permutation and bootstraps) Equations are given for all analyses, and R statistical code provided for many of the analyses Sample size calculations for evidential probabilities of misleading and weak evidence are explained Useful techniques, like Matthews’s critical prior interval, Goodman’s Bayes factor, and Armitage’s stopping rule are described Recommended for undergraduate and graduate students in any field that relies heavily on statistical analysis, as well as active researchers and professionals in those fields, Evidence-Based Statistics: An Introduction to the Evidential Approach – from Likelihood Principle to Statistical Practice belongs on the bookshelf of anyone who wants to amplify and empower their approach to statistical analysis.
