Introduction to Nonparametric Statistics for the Biological Sciences Using R

Nonfiction, Health & Well Being, Medical, Reference, Biostatistics, Science & Nature, Mathematics, Computers, Application Software
Cover of the book Introduction to Nonparametric Statistics for the Biological Sciences Using R by Thomas W. MacFarland, Jan M. Yates, Springer International Publishing
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Author: Thomas W. MacFarland, Jan M. Yates ISBN: 9783319306346
Publisher: Springer International Publishing Publication: July 6, 2016
Imprint: Springer Language: English
Author: Thomas W. MacFarland, Jan M. Yates
ISBN: 9783319306346
Publisher: Springer International Publishing
Publication: July 6, 2016
Imprint: Springer
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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:

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.

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