000			03107nam a2200421 i 4500
001			CR9781139194648
003			UkCbUP
005			20240906164814.0
006			m|||||o||d||||||||
007			cr||||||||||||
008			111109s2010||||enk o ||1 0|eng|d
020			_a9781139194648 (ebook)
020			_z9780521762939 (hardback)
040			_aUkCbUP _beng _erda _cUkCbUP
050	0	0	_aQA276.4 _b.M245 2010
082	0	0	_a519.50285 _222
100	1		_aMaindonald, J. H. _q(John Hilary), _d1937- _eauthor.
245	1	0	_aData analysis and graphics using R : _ban example-based approach / _cJohn Maindonald and W. John Braun.
246	3		_aData Analysis & Graphics Using R
250			_aThird edition.
264		1	_aCambridge : _bCambridge University Press, _c2010.
300			_a1 online resource (xxvi, 525 pages) : _bdigital, PDF file(s).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
490	1		_aCambridge series on statistical and probabilistic mathematics ; _v10
500			_aTitle from publisher's bibliographic system (viewed on 05 Oct 2015).
505	0		_aA brief introduction to R -- Styles of data analysis -- Statistical models -- A review of inference concepts -- Regression with a single predictor -- Multiple linear regression -- Exploiting the linear model framework -- Generalized linear models and survival analysis -- Time series models -- Multi-level models, and repeated measures -- Tree-based classification and regression -- Multivariate data exploration and discrimination -- Regression on principal component or discriminant scores -- The R system: additional topics -- Graphs in R.
520			_aDiscover what you can do with R! Introducing the R system, covering standard regression methods, then tackling more advanced topics, this book guides users through the practical, powerful tools that the R system provides. The emphasis is on hands-on analysis, graphical display, and interpretation of data. The many worked examples, from real-world research, are accompanied by commentary on what is done and why. The companion website has code and datasets, allowing readers to reproduce all analyses, along with solutions to selected exercises and updates. Assuming basic statistical knowledge and some experience with data analysis (but not R), the book is ideal for research scientists, final-year undergraduate or graduate-level students of applied statistics, and practising statisticians. It is both for learning and for reference. This third edition expands upon topics such as Bayesian inference for regression, errors in variables, generalized linear mixed models, and random forests.
650		0	_aStatistics _xData processing.
650		0	_aStatistics _xGraphic methods _xData processing.
650		0	_aR (Computer program language)
700	1		_aBraun, John, _d1963- _eauthor.
776	0	8	_iPrint version: _z9780521762939
830		0	_aCambridge series on statistical and probabilistic mathematics ; _v10.
856	4	0	_uhttps://doi.org/10.1017/CBO9781139194648
942			_2ddc _cEB
999			_c10112 _d10112