Regression [recurso electrónico] : Linear Models in Statistics / by N. H. Bingham, John M. Fry.

Por: Bingham, N. H [author.]Colaborador(es): Fry, John M [author.] | SpringerLink (Online service)Tipo de material: TextoTextoSeries Springer Undergraduate Mathematics SeriesEditor: London : Springer London : Imprint: Springer, 2010Descripción: XIII, 284p. 50 illus. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9781848829695Tema(s): Mathematics | Mathematical statistics | Mathematics | Applications of Mathematics | Statistical Theory and MethodsFormatos físicos adicionales: Printed edition:: Sin títuloClasificación CDD: 519 Clasificación LoC:T57-57.97Recursos en línea: Libro electrónicoTexto
Contenidos:
Linear 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.
En: Springer eBooksResumen: Regression 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.
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Tipo de ítem Biblioteca actual Colección Signatura Copia número Estado Fecha de vencimiento Código de barras
Libro Electrónico Biblioteca Electrónica
Colección de Libros Electrónicos T57 -57.97 (Browse shelf(Abre debajo)) 1 No para préstamo 372790-2001

Linear 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.

Regression 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.

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