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101 0 $aeng
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200 10$aIntroduction to Nonparametric Statistics for the Biological Sciences Using R /$fby Thomas W. MacFarland, Jan M. Yates
205   $a1st ed. 2016.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2016.
215   $a1 online resource (XV, 329 p. 65 illus., 64 illus. in color.)
300   $aIncludes index.
311 08$a3-319-30633-2 
327   $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.
330   $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 forthe 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.
606   $aBiometry
606   $aMathematical statistics$xData processing
606   $aAgriculture
606   $aStatistics
606   $aBiostatistics
606   $aStatistics and Computing
606   $aAgriculture
606   $aStatistical Theory and Methods
615  0$aBiometry.
615  0$aMathematical statistics$xData processing.
615  0$aAgriculture.
615  0$aStatistics.
615 14$aBiostatistics.
615 24$aStatistics and Computing.
615 24$aAgriculture.
615 24$aStatistical Theory and Methods.
676   $a519.5
700   $aMacFarland$b Thomas W.$4aut$4http://id.loc.gov/vocabulary/relators/aut$0721661
702   $aYates$b Jan M.$4aut$4http://id.loc.gov/vocabulary/relators/aut
801  0$bMiAaPQ
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906   $aBOOK
912   $a9910254064003321
996   $aIntroduction to Nonparametric Statistics for the Biological Sciences Using R$91963838
997   $aUNINA