This book contains a rich set of tools for nonparametric analyses, and the purpose of this supplemental text is to provide guidance to students and professional researchers on how R is used for nonparametric data analysis in the biological sciences:
The book focuses on how R is used to distinguish between data that could be classified as nonparametric as opposed to data that could be classified as parametric, with both approaches to data classification covered extensively.
Following an introductory lesson on nonparametric statistics for the biological sciences, the book is organized into eight self-contained lessons on various analyses and tests using R to broadly compare differences between data sets and statistical approach.
This supplemental text is intended for:
Chapter 1 Nonparametric Statistics for the Biological Sciences
Chapter 2 Sign Test
Chapter 3 Chi-Square
Chapter 4 Mann–Whitney U Test
Chapter 5 Wilcoxon Matched-Pairs Signed-Ranks Test
Chapter 6 Kruskal–Wallis H-Test for Oneway Analysis of Variance (ANOVA) by Ranks
Chapter 7 Friedman Twoway Analysis of Variance (ANOVA) by Ranks
Chapter 8 Spearman’s Rank-Difference Coefficient of Correlation
Chapter 9 Other Nonparametric Tests for the Biological Sciences