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Regression linear models in statistics / N.H. Bingham, John M. Fry.

By: Contributor(s): Material type: TextTextLanguage: English Series: Springer undergraduate mathematics seriesPublication details: London ; New York : Springer, c2010.ISBN:
  • 9781848829688 (pbk.)
  • 184882968X (pbk.)
  • 9781848829695 (eISBN)
  • 1848829698 (eISBN)
Subject(s): DDC classification:
  • 519.536 22
Contents:
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 -- Solutions -- Dramatis personae : who did what when.
SpringerLink ebooks - Mathematics and Statistics (2010)Review: "The Springer Undergraduate Mathematics Series (SUMS) is designed for undergraduates in the mathematical sciences. From core foundational material to final year topics, SUMS books take a fresh and modern approach and are ideal for self-study or for a one-or two-semester course. Each book includes numerous examples, problems and fully-worked solutions. N. H. Bingham. John M. Fry Regression" "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."--BOOK JACKET.
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Item type Current library Call number Copy number Status Date due Barcode
General Books General Books CUTN Central Library Sciences 519.536 (Browse shelf(Opens below)) 1 Available 10787

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 -- Solutions -- Dramatis personae : who did what when.

Includes bibliographical references and index.

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"The Springer Undergraduate Mathematics Series (SUMS) is designed for undergraduates in the mathematical sciences. From core foundational material to final year topics, SUMS books take a fresh and modern approach and are ideal for self-study or for a one-or two-semester course. Each book includes numerous examples, problems and fully-worked solutions. N. H. Bingham. John M. Fry Regression" "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."--BOOK JACKET.

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