000 03153nam a22003855i 4500
001 291270
003 MX-SnUAN
005 20160429154840.0
007 cr nn 008mamaa
008 150903s2010 xxk| o |||| 0|eng d
020 _a9781848829695
_99781848829695
024 7 _a10.1007/9781848829695
_2doi
035 _avtls000344584
039 9 _a201509030355
_bVLOAD
_c201405050309
_dVLOAD
_y201402061300
_zstaff
040 _aMX-SnUAN
_bspa
_cMX-SnUAN
_erda
050 4 _aT57-57.97
100 1 _aBingham, N. H.
_eautor
_9322480
245 1 0 _aRegression :
_bLinear Models in Statistics /
_cby N. H. Bingham, John M. Fry.
264 1 _aLondon :
_bSpringer London :
_bImprint: Springer,
_c2010.
300 _axiii, 284 páginas 50 ilustraciones
_brecurso en línea.
336 _atexto
_btxt
_2rdacontent
337 _acomputadora
_bc
_2rdamedia
338 _arecurso en línea
_bcr
_2rdacarrier
347 _aarchivo de texto
_bPDF
_2rda
490 0 _aSpringer Undergraduate Mathematics Series,
_x1615-2085
500 _aSpringer eBooks
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.
590 _aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700 1 _aFry, John M.
_eautor
_9322481
710 2 _aSpringerLink (Servicio en línea)
_9299170
776 0 8 _iEdición impresa:
_z9781848829688
856 4 0 _uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-1-84882-969-5
_zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
942 _c14
999 _c291270
_d291270