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Linear models and regression with R [electronic resource] : an integrated approach / Debasis Sengupta, Sreenivasa Rao Jammalamadaka.

By: Sengupta, Debasis, 1949-.
Contributor(s): Jammalamadaka, S. Rao | Sengupta, Debasis, 1949-. Linear models.
Material type: materialTypeLabelBookSeries: Series on multivariate analysis, v. 11.Publisher: Singapore : World Scientific Publishing Co. Pte Ltd., [2019], ©2020Description: 1 online resource (772 p.) : ill.ISBN: 9789811200410.Subject(s): Linear models (Statistics) | Regression analysis | Linear models (Statistics) -- Data processing | R (Computer program language) | Electronic booksDDC classification: 519.5/360285513 Online resources: Access to full text is restricted to subscribers. Summary: "Starting with the basic linear model where the design and covariance matrices are of full rank, this book demonstrates how the same statistical ideas can be used to explore the more general linear model with rank-deficient design and/or covariance matrices. The unified treatment presented here provides a clearer understanding of the general linear model from a statistical perspective, thus avoiding the complex matrix-algebraic arguments that are often used in the rank-deficient case. Elegant geometric arguments are used as needed. The book has a very broad coverage, from illustrative practical examples in Regression and Analysis of Variance alongside their implementation using R, to providing comprehensive theory of the general linear model with 181 worked-out examples, 227 exercises with solutions, 152 exercises without solutions (so that they may be used as assignments in a course), and 320 up-to-date references."-- Publisher's website.
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Mode of access: World Wide Web.

System requirements: Adobe Acrobat Reader.

Title from title screen (World Scientific, viewed October 29, 2019).

"This book is an expanded, updated, and reorganized version of 'Linear Models: An Integrated Approach', published sixteen years ago."--Preface.

Includes bibliographical references and indexes.

"Starting with the basic linear model where the design and covariance matrices are of full rank, this book demonstrates how the same statistical ideas can be used to explore the more general linear model with rank-deficient design and/or covariance matrices. The unified treatment presented here provides a clearer understanding of the general linear model from a statistical perspective, thus avoiding the complex matrix-algebraic arguments that are often used in the rank-deficient case. Elegant geometric arguments are used as needed. The book has a very broad coverage, from illustrative practical examples in Regression and Analysis of Variance alongside their implementation using R, to providing comprehensive theory of the general linear model with 181 worked-out examples, 227 exercises with solutions, 152 exercises without solutions (so that they may be used as assignments in a course), and 320 up-to-date references."-- Publisher's website.

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