000			03159nam a22004575i 4500
001			978-1-84882-969-5
003			DE-He213
005			20140220084515.0
007			cr nn 008mamaa
008			100917s2010 xxk| s |||| 0|eng d
020			_a9781848829695 _9978-1-84882-969-5
024	7		_a10.1007/978-1-84882-969-5 _2doi
050		4	_aT57-57.97
072		7	_aPBW _2bicssc
072		7	_aMAT003000 _2bisacsh
082	0	4	_a519 _223
100	1		_aBingham, N. H. _eauthor.
245	1	0	_aRegression _h[electronic resource] : _bLinear Models in Statistics / _cby N. H. Bingham, John M. Fry.
264		1	_aLondon : _bSpringer London : _bImprint: Springer, _c2010.
300			_aXIII, 284p. 50 illus. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aSpringer Undergraduate Mathematics Series, _x1615-2085
505	0		_aLinear Regression -- The Analysis of Variance (ANOVA) -- Multiple Regression -- Further Multilinear Regression -- Adding additional covariates and the Analysis of Covariance -- Linear Hypotheses -- Model Checking and Transformation of Data -- Generalised Linear Models -- Other topics.
520			_aRegression is the branch of Statistics in which a dependent variable of interest is modelled as a linear combination of one or more predictor variables, together with a random error. The subject is inherently two- or higher- dimensional, thus an understanding of Statistics in one dimension is essential. Regression: Linear Models in Statistics fills the gap between introductory statistical theory and more specialist sources of information. In doing so, it provides the reader with a number of worked examples, and exercises with full solutions. The book begins with simple linear regression (one predictor variable), and analysis of variance (ANOVA), and then further explores the area through inclusion of topics such as multiple linear regression (several predictor variables) and analysis of covariance (ANCOVA). The book concludes with special topics such as non-parametric regression and mixed models, time series, spatial processes and design of experiments. Aimed at 2nd and 3rd year undergraduates studying Statistics, Regression: Linear Models in Statistics requires a basic knowledge of (one-dimensional) Statistics, as well as Probability and standard Linear Algebra. Possible companions include John Haigh’s Probability Models, and T. S. Blyth & E.F. Robertsons’ Basic Linear Algebra and Further Linear Algebra.
650		0	_aMathematics.
650		0	_aMathematical statistics.
650	1	4	_aMathematics.
650	2	4	_aApplications of Mathematics.
650	2	4	_aStatistical Theory and Methods.
700	1		_aFry, John M. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9781848829688
830		0	_aSpringer Undergraduate Mathematics Series, _x1615-2085
856	4	0	_uhttp://dx.doi.org/10.1007/978-1-84882-969-5
912			_aZDB-2-SMA
999			_c110932 _d110932