Overview
- Eight self-contained lessons instructing how to use R 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
- From data to final interpretation of outcomes - starts with a simple real-world data set from the biological sciences and outlines step-by-step guidance on how R can be used to address nonparametric data analysis and the generation of graphical images to promote effective communication of outcomes
- Focuses on data review and accompanying data quality review processes - so that outcomes can be trusted and hold up to peer review
- Includes supplementary material: sn.pub/extras
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Table of contents (9 chapters)
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Front Matter
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Back Matter
Keywords
About this book
This book contains a rich set of tools for nonparametric analyses, and the purpose of this 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.
Authors and Affiliations
About the authors
Jan Yates, Ph.D., is Associate Professor of Educational Media and Computer Science Education at Nova Southeastern University's Abraham S. Fischler College of Education in Fort Lauderdale, Florida. Since 2001, she has worked in the areas of curriculum development, program assessment and review, and accreditation.
Bibliographic Information
Book Title: Introduction to Nonparametric Statistics for the Biological Sciences Using R
Authors: Thomas W. MacFarland, Jan M. Yates
DOI: https://doi.org/10.1007/978-3-319-30634-6
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer Nature Switzerland AG 2016
Hardcover ISBN: 978-3-319-30633-9Published: 16 July 2016
Softcover ISBN: 978-3-319-80856-7Published: 07 June 2018
eBook ISBN: 978-3-319-30634-6Published: 06 July 2016
Edition Number: 1
Number of Pages: XV, 329
Number of Illustrations: 1 b/w illustrations, 64 illustrations in colour
Topics: Statistics for Life Sciences, Medicine, Health Sciences, Statistics and Computing/Statistics Programs, Biostatistics, Agriculture, Statistical Theory and Methods