大葉大學圖書館 |

Introduction to nonparametric statistics for the biological sciences using R[electronic resource] /

紀錄類型:	書目-電子資源 : Monograph/item
杜威分類號:	519.54
書名/作者:	Introduction to nonparametric statistics for the biological sciences using R/ by Thomas W. MacFarland, Jan M. Yates.
作者:	MacFarland, Thomas W.
其他作者:	Yates, Jan M.
出版者:	Cham : : Springer International Publishing :, 2016.
面頁冊數:	xv, 329 p. : : ill. (some col.), digital ;; 24 cm.
Contained By:	Springer eBooks
標題:	Nonparametric statistics.
標題:	R (Computer program language)
標題:	Statistics.
標題:	Statistics for Life Sciences, Medicine, Health Sciences.
標題:	Statistics and computing/Statistics Programs.
標題:	Biostatistics.
標題:	Agriculture.
標題:	Statistical Theory and Methods.
ISBN:	9783319306346
ISBN:	9783319306339
內容註:	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.
摘要、提要註:	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.
電子資源:	http://dx.doi.org/10.1007/978-3-319-30634-6

Introduction to nonparametric statistics for the biological sciences using R[electronic resource] /
MacFarland, Thomas W.

Introduction to nonparametric statistics for the biological sciences using R[electronic resource] /by Thomas W. MacFarland, Jan M. Yates. - Cham :Springer International Publishing :2016. - xv, 329 p. :ill. (some col.), digital ;24 cm.

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.

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.

ISBN: 9783319306346

Standard No.: 10.1007/978-3-319-30634-6doiSubjects--Topical Terms:

405341
Nonparametric statistics.

LC Class. No.: QA278.8

Dewey Class. No.: 519.54

Introduction to nonparametric statistics for the biological sciences using R[electronic resource] /
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