000			03269nam a22004335i 4500
001			978-0-387-09608-7
003			DE-He213
005			20250710083924.0
007			cr nn 008mamaa
008			110413s2009 xxu| s |||| 0|eng d
020			_a9780387096087 _a99780387096087
024	7		_a10.1007/978-0-387-09608-7 _2doi
082	0	4	_a519.5 _223
100	1		_aSheather, Simon. _eauthor.
245	1	2	_aA Modern Approach to Regression with R _h[recurso electrónico] / _cby Simon Sheather.
264		1	_aNew York, NY : _bSpringer New York : _bImprint: Springer, _c2009.
300			_bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aSpringer Texts in Statistics, _x1431-875X
505	0		_aSimple Linear Regression -- Diagnostics and Transformations for Simple Linear Regression -- Weighted Least Squares -- Multiple Linear Regression -- Diagnostics and Transformations for Multiple Linear Regression -- Variable Selection -- Logistic Regression -- Serially Correlated Errors -- Mixed Models.
520			_aA Modern Approach to Regression with R focuses on tools and techniques for building regression models using real-world data and assessing their validity. A key theme throughout the book is that it makes sense to base inferences or conclusions only on valid models. The regression output and plots that appear throughout the book have been generated using R. On the book website you will find the R code used in each example in the text. You will also find SAS-code and STATA-code to produce the equivalent output on the book website. Primers containing expanded explanations of R, SAS and STATA and their use in this book are also available on the book website. The book contains a number of new real data sets from applications ranging from rating restaurants, rating wines, predicting newspaper circulation and magazine revenue, comparing the performance of NFL kickers, and comparing finalists in the Miss America pageant across states. One of the aspects of the book that sets it apart from many other regression books is that complete details are provided for each example. The book is aimed at first year graduate students in statistics and could also be used for a senior undergraduate class. Simon Sheather is Professor and Head of the Department of Statistics at Texas A&M University. Professor Sheather's research interests are in the fields of flexible regression methods and nonparametric and robust statistics. He is a Fellow of the American Statistical Association and listed on ISIHighlyCited.com.
650		0	_aSTATISTICS.
650		0	_aMATHEMATICAL STATISTICS.
650		0	_aECONOMETRICS.
650	1	4	_aSTATISTICS.
650	2	4	_aSTATISTICAL THEORY AND METHODS.
650	2	4	_aECONOMETRICS.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9780387096070
830		0	_aSpringer Texts in Statistics, _x1431-875X
856	4	0	_uhttp://dx.doi.org/10.1007/978-0-387-09608-7 _zVer el texto completo en las instalaciones del CICY
912			_aZDB-2-SMA
942			_2ddc _cER
999			_c55988 _d55988