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Introduction to nonparametric statistics for the biological sciences using R. (Record no. 103193)

MARC details
000 -LEADER
fixed length control field	02739nam a22001937a 4500
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number	978-3319306339
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number	519.54 MAC
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name	MacFarland, Thomas W.; Yates, Jan M.
245 ## - TITLE STATEMENT
Title	Introduction to nonparametric statistics for the biological sciences using R.
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc.	USA
Name of publisher, distributor, etc.	Springer
Date of publication, distribution, etc.	2016
300 ## - PHYSICAL DESCRIPTION
Extent	xv, 329 p.
Other physical details	23 cm ; Hard
500 ## - GENERAL NOTE
General note	Alpha/2434/Rs.4957/-
505 ## - FORMATTED CONTENTS NOTE
Formatted contents note	Chapter 1 Nonparametric Statistics for the Biological Sciences --<br/>Chapter 2 Sign Test --<br/>Chapter 3 Chi-Square --<br/>Chapter 4 Mann-Whitney U Test --<br/>Chapter 5 Wilcoxon Matched-Pairs Signed-Ranks Test --<br/>Chapter 6 Kruskal-Wallis H-Test for Oneway Analysis of Variance (ANOVA) by Ranks --<br/>Chapter 7 Friedman Twoway Analysis of Variance (ANOVA) by Ranks <br/>Chapter 8 Spearman's Rank-Difference Coefficient of Correlation <br/>Chapter 9 Other Nonparametric Tests for the Biological Sciences.
520 ## - SUMMARY, ETC.
Summary, etc.	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 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element	Nonparametric statistics
--	R (Computer program language)
--	Statistics
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name	MacFarland, Thomas W.;Yates, Jan M.
856 ## - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier	<a href="http://www.springer.com/in/book/9783319306339">http://www.springer.com/in/book/9783319306339</a>
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type	Books

Holdings
Withdrawn status	Lost status	Damaged status	Not for loan	Collection code	Home library	Current library	Shelving location	Date acquired	Source of acquisition	Cost, normal purchase price	Total Checkouts	Full call number	Barcode	Date last seen	Price effective from	Koha item type
				GSB Collection				17/06/2017	Prof. Hemalatha	0.00		519.54 MAC	B2400	17/06/2017	17/06/2017	Books