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Introduction to Nonparametric Statistics for the Biological Sciences Using R

  • Thomas W. MacFarland
  • Jan M. Yates

Table of contents

  1. Front Matter
    Pages i-xv
  2. Thomas W. MacFarland, Jan M. Yates
    Pages 1-50
  3. Thomas W. MacFarland, Jan M. Yates
    Pages 51-76
  4. Thomas W. MacFarland, Jan M. Yates
    Pages 77-102
  5. Thomas W. MacFarland, Jan M. Yates
    Pages 103-132
  6. Thomas W. MacFarland, Jan M. Yates
    Pages 133-175
  7. Thomas W. MacFarland, Jan M. Yates
    Pages 177-211
  8. Thomas W. MacFarland, Jan M. Yates
    Pages 213-247
  9. Thomas W. MacFarland, Jan M. Yates
    Pages 249-297
  10. Thomas W. MacFarland, Jan M. Yates
    Pages 299-326
  11. Back Matter
    Pages 327-329

About this book

Introduction

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:

  • To introduce when nonparametric approaches to data analysis are appropriate
  • To introduce the leading nonparametric tests commonly used in biostatistics and how R is used to generate appropriate statistics for each test
  • To introduce common figures typically associated with nonparametric data analysis and how R is used to generate appropriate figures in support of each data set

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:

  • Upper-level undergraduate and graduate students majoring in the biological sciences, specifically those in agriculture, biology, and health science - both students in lecture-type courses and also those engaged in research projects, such as a master's thesis or a doctoral dissertation
  • And biological researchers at the professional level without a nonparametric statistics background but who regularly work with data more suitable to a nonparametric approach to data analysis

Keywords

R Nonparametric Normal distribution Mann-Whitney U-Test Wilcoxon Matched-Pairs Signed Ranks Test Kruskal-Wallis H-Test Oneway ANOVA Ranks Life Sciences Biological Sciences Agriculture Biotechnology Plant Science Biostatistics

Authors and affiliations

  • Thomas W. MacFarland
    • 1
  • Jan M. Yates
    • 2
  1. 1.Office of Institutional EffectivenessNova Southeastern UniversityFort LauderdaleUSA
  2. 2.Abraham S. Fischler College of EducationNova Southeastern UniversityFort LauderdaleUSA

Bibliographic information