000			02739nam a22001937a 4500
020			_a978-3319306339
082			_a519.54 MAC
100			_aMacFarland, Thomas W.; Yates, Jan M.
245			_a Introduction to nonparametric statistics for the biological sciences using R.
260			_aUSA _bSpringer _c2016
300			_axv, 329 p. _b23 cm ; Hard
500			_aAlpha/2434/Rs.4957/-
505			_aChapter 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			_aThis 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			_aNonparametric statistics _aR (Computer program language) _aStatistics
700			_aMacFarland, Thomas W.;Yates, Jan M.
856			_uhttp://www.springer.com/in/book/9783319306339
942			_cBK
999			_c103193 _d103193