000			03017cam a2200373 i 4500
001			21458508
003			OSt
005			20221117142338.0
008			200222t20202020njua b 001 0 eng d
010			_a 2019051985
020			_a9781119549932 _qhardcover
040			_aLBSOR/DLC _beng _erda _cDLC _dUOC
042			_apcc
082	0	0	_a519.5302855133 _223 _bDAN
100	1		_aDenis, Daniel J., _d1974- _eauthor. _93431
245	1	0	_aUnivariate, bivariate, and multivariate statistics using R : _bquantitative tools for data analysis and data science / _cDaniel J. Denis.
250			_aFirst Edition.
264		1	_aHoboken, NJ : _bWiley, _c2020.
264		4	_c© 2020 John Wiley & Sons, Inc.
300			_axvii, 366 pages : _billustrations ; _c24 cm.
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
504			_aIncludes bibliographical references and index.
505	0		_aIntroduction to applied statistics -- Introduction to R and computational statistics -- Exploring data with R : essential graphics and visualization -- Means, correlations, counts : drawing inferences using easy-to-implement statistical tests -- Power analysis and sample size estimation using R -- Analysis of variance : fixed effects, random effects, mixed models and repeated measures -- Simple and multiple linear regression -- Logistic regression and the generalized linear model -- Multivariate analysis of variance (MANOVA) and discriminant analysis -- Principal components analysis -- Exploratory factor analysis -- Cluster analysis -- Nonparametric tests.
520			_a"This book provides a user-friendly and practical guide on R, with emphasis on covering a broader range of statistical methods than previous books on R. This is a "how to" book and will be of use to undergraduates and graduate students along with researchers and professionals who require a quick go-to source to help them perform essential statistical analyses and data management tasks in R. The book only assumes minimal prior knowledge of statistics, providing readers with the tools they need right now to help them understand and interpret their data analyses. This book covers univariate, bivariate, and multivariate statistical methods, as well as some nonparametric tests. It provides students with a hands-on easy-to-read manual on the wealth of applied statistics and essential R computing that they will need for their theses, dissertations, and research publications. A strength of this book is its scope of coverage of univariate through to multivariate procedures, while simultaneously serving as a friendly introduction to R software"-- _cProvided by publisher.
650		0	_aAnalysis of variance. _93433
650		0	_aMultivariate analysis. _93365
650		0	_aMathematical statistics _xData processing. _93432
650		0	_aR (Computer program language) _984
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg
942			_2ddc _cTEXT BOOK
999			_c1402 _d1402