國立虎尾科技大學 |

Introduction to nonparametric statistics for the biological sciences using R

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Introduction to nonparametric statistics for the biological sciences using R/ by Thomas W. MacFarland, Jan M. Yates.
Author:	MacFarland, Thomas W.
other author:	Yates, Jan M.
Published:	Cham :Springer International Publishing : : 2016.,
Description:	xv, 329 p. :ill. (some col.), digital ; : 24 cm.;
Contained By:	Springer eBooks
Subject:	Nonparametric statistics. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-30634-6
ISBN:	9783319306346

Introduction to nonparametric statistics for the biological sciences using R
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:

527779
Nonparametric statistics.

LC Class. No.: QA278.8

Dewey Class. No.: 519.54

Introduction to nonparametric statistics for the biological sciences using R
LDR:03132nam a2200325 a 4500 001 865902
003 DE-He213
005 20160706094607.0
006 m d
007 cr nn 008maaau
008 170720s2016 gw s 0 eng d
020 $a 9783319306346 $q (electronic bk.)
020 $a 9783319306339 $q (paper)
024 7 $a 10.1007/978-3-319-30634-6 $2 doi
035 $a 978-3-319-30634-6
040 $a GP $c GP
041 0 $a eng
050 4 $a QA278.8
072 7 $a PBT $2 bicssc
072 7 $a MBNS $2 bicssc
072 7 $a MED090000 $2 bisacsh
082 0 4 $a 519.54 $2 23
090 $a QA278.8 $b .M143 2016
100 1 $a MacFarland, Thomas W. $3 889346
245 1 0 $a Introduction to nonparametric statistics for the biological sciences using R $h [electronic resource] / $c by Thomas W. MacFarland, Jan M. Yates.
260 $a Cham : $c 2016. $b Springer International Publishing : $b Imprint: Springer,
300 $a xv, 329 p. : $b ill. (some col.), digital ; $c 24 cm.
505 0 $a 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.
520 $a 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.
650 0 $a Nonparametric statistics. $3 527779
650 0 $a R (Computer program language) $3 679069
650 1 4 $a Statistics. $3 556824
650 2 4 $a Statistics for Life Sciences, Medicine, Health Sciences. $3 670172
650 2 4 $a Statistics and computing/Statistics Programs. $3 1111738
650 2 4 $a Biostatistics. $3 783654
650 2 4 $a Agriculture. $3 660421
650 2 4 $a Statistical Theory and Methods. $3 671396
700 1 $a Yates, Jan M. $3 584153
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer eBooks
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-30634-6
950 $a Mathematics and Statistics (Springer-11649)