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001 978-3-319-30634-6
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020 _a9783319306346
_9978-3-319-30634-6
024 7 _a10.1007/978-3-319-30634-6
_2doi
040 _aISI Library, Kolkata
050 4 _aQA276-280
072 7 _aPBT
_2bicssc
072 7 _aMED090000
_2bisacsh
072 7 _aPBT
_2thema
072 7 _aMBNS
_2thema
082 0 4 _a519.5
_223
100 1 _aMacFarland, Thomas W.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aIntroduction to Nonparametric Statistics for the Biological Sciences Using R
_h[electronic resource] /
_cby Thomas W. MacFarland, Jan M. Yates.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2016.
300 _aXV, 329 p. 65 illus., 64 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _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 0 _aStatistics.
650 0 _aMathematical statistics.
650 0 _aStatistical methods.
650 0 _aAgriculture.
650 1 4 _aStatistics for Life Sciences, Medicine, Health Sciences.
_0http://scigraph.springernature.com/things/product-market-codes/S17030
650 2 4 _aStatistics and Computing/Statistics Programs.
_0http://scigraph.springernature.com/things/product-market-codes/S12008
650 2 4 _aBiostatistics.
_0http://scigraph.springernature.com/things/product-market-codes/L15020
650 2 4 _aAgriculture.
_0http://scigraph.springernature.com/things/product-market-codes/L11006
650 2 4 _aStatistical Theory and Methods.
_0http://scigraph.springernature.com/things/product-market-codes/S11001
700 1 _aYates, Jan M.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319306339
776 0 8 _iPrinted edition:
_z9783319306353
776 0 8 _iPrinted edition:
_z9783319808567
856 4 0 _uhttps://doi.org/10.1007/978-3-319-30634-6
912 _aZDB-2-SMA
942 _cEB
950 _aMathematics and Statistics (Springer-11649)
999 _c426617
_d426617