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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.
Inhaltsverzeichnis
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.
Über den Autor / die Autorin
Thomas W. MacFarland, Ed.D., is Associate Professor (Computer Technology) at Nova Southeastern University in Fort Lauderdale, Florida. He joined the Graduate School of Computer and Information Sciences in 1988 and provides consulting services to the university community on research methods and statistical design as well as individual research on institutional concerns and assessment of student learning. Dr. MacFarland's areas of research include institutional research, assessment of student learning outcomes, federal data resources, and K-12 computer science education.
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.
Zusammenfassung
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.
