Introduction to Nonparametric Statistics for the Biological Sciences Using R
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Beschreibung
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 forthe 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
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.
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
Autor*in
Thomas W. MacFarland
Themen in »Introduction to Nonparametric Statistics for the Biological Sciences Using R«
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 Biotechnology Plant Science