Linear Models in Statistics

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9780471754985: Linear Models in Statistics

The essential introduction to the theory and application of linear models-now in a valuable new edition Since most advanced statistical tools are generalizations of the linear model, it is neces-sary to first master the linear model in order to move forward to more advanced concepts. The linear model remains the main tool of the applied statistician and is central to the training of any statistician regardless of whether the focus is applied or theoretical. This completely revised and updated new edition successfully develops the basic theory of linear models for regression, analysis of variance, analysis of covariance, and linear mixed models. Recent advances in the methodology related to linear mixed models, generalized linear models, and the Bayesian linear model are also addressed. Linear Models in Statistics, Second Edition includes full coverage of advanced topics, such as mixed and generalized linear models, Bayesian linear models, two-way models with empty cells, geometry of least squares, vector-matrix calculus, simultaneous inference, and logistic and nonlinear regression. Algebraic, geometrical, frequentist, and Bayesian approaches to both the inference of linear models and the analysis of variance are also illustrated. Through the expansion of relevant material and the inclusion of the latest technological developments in the field, this book provides readers with the theoretical foundation to correctly interpret computer software output as well as effectively use, customize, and understand linear models. This modern Second Edition features:* New chapters on Bayesian linear models as well as random and mixed linear models* Expanded discussion of two-way models with empty cells* Additional sections on the geometry of least squares* Updated coverage of simultaneous inference The book is complemented with easy-to-read proofs, real data sets, and an extensive bibliography. A thorough review of the requisite matrix algebra has been addedfor transitional purposes, and numerous theoretical and applied problems have been incorporated with selected answers provided at the end of the book. A related Web site includes additional data sets and SAS(r) code for all numerical examples. Linear Model in Statistics, Second Edition is a must-have book for courses in statistics, biostatistics, and mathematics at the upper-undergraduate and graduate levels. It is also an invaluable reference for researchers who need to gain a better understanding of regression and analysis of variance.

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Recensione:

"This indeed clearly written book will do great service for advanced undergraduate and also for PhD students." ( International Statistical Review, December 2008)

"This indeed clearly written book will do great service for advanced undergraduate and also for PhD students." (International Statistical Review, Dec 2008)

"This well–written book represents various topics on linear models with great clarity in an easy–to–understand style." (CHOICE, Aug 2008)



"This indeed clearly written book will do great service for advanced undergraduate and also for PhD students." ( International Statistical Review, Dec 2008)

"This well–written book represents various topics on linear models with great clarity in an easy–to–understand style." (CHOICE, Aug 2008)

L'autore:

Alvin C. Rencher, PhD, is Professor of Statistics at Brigham Young University. Dr. Rencher is a Fellow of the American Statistical Association and the author of Methods of Multivariate Analysis and Multivariate Statistical Inference and Applications, both published by Wiley.
 
G. Bruce Schaalje, PhD, is Professor of Statistics at Brigham Young University. He has authored over 120 journal articles in his areas of research interest, which include mixed linear models, small sample inference, and design of experiments.

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Alvin C. Rencher/ G. Bruce Schaalje
Editore: Wiley-Interscience (2008)
ISBN 10: 0471754986 ISBN 13: 9780471754985
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Descrizione libro Wiley-Interscience, 2008. Hardcover. Condizione libro: Brand New. 2nd edition. 672 pages. 9.25x6.25x1.50 inches. In Stock. Codice libro della libreria __0471754986

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Rencher, Alvin C.;Schaalje, G. Bruce
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Descrizione libro Hoboken, New Jersey, U.S.A.: Wiley-Interscience, 2008. Hardcover. Condizione libro: New. Ship out 24 hours,Brand new,US edition, (Express shipping worldwide)2-4 biz days delivery to worldwide Same shipping fee with US, Canada,Europe country, Australia, item will ship out from either USA or Asia. Codice libro della libreria ABE-6337107610

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Alvin C. Rencher (Brigham Young Univ., Provo, Utah); G. Bruce Schaalje (Brigham Young Univ., Provo, UT)
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Descrizione libro Wileyand#8211;Blackwell, 2008. HRD. Condizione libro: New. New Book. Shipped from UK in 4 to 14 days. Established seller since 2000. Codice libro della libreria FW-9780471754985

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Descrizione libro John Wiley and Sons Ltd, United Kingdom, 2008. Hardback. Condizione libro: New. 2nd Revised edition. 239 x 160 mm. Language: English . Brand New Book. The essential introduction to the theory and application of linear models-now in a valuable new edition Since most advanced statistical tools are generalizations of the linear model, it is neces-sary to first master the linear model in order to move forward to more advanced concepts. The linear model remains the main tool of the applied statistician and is central to the training of any statistician regardless of whether the focus is applied or theoretical. This completely revised and updated new edition successfully develops the basic theory of linear models for regression, analysis of variance, analysis of covariance, and linear mixed models. Recent advances in the methodology related to linear mixed models, generalized linear models, and the Bayesian linear model are also addressed. Linear Models in Statistics, Second Edition includes full coverage of advanced topics, such as mixed and generalized linear models, Bayesian linear models, two-way models with empty cells, geometry of least squares, vector-matrix calculus, simultaneous inference, and logistic and nonlinear regression.Algebraic, geometrical, frequentist, and Bayesian approaches to both the inference of linear models and the analysis of variance are also illustrated. Through the expansion of relevant material and the inclusion of the latest technological developments in the field, this book provides readers with the theoretical foundation to correctly interpret computer software output as well as effectively use, customize, and understand linear models. This modern Second Edition features:* New chapters on Bayesian linear models as well as random and mixed linear models* Expanded discussion of two-way models with empty cells* Additional sections on the geometry of least squares* Updated coverage of simultaneous inference The book is complemented with easy-to-read proofs, real data sets, and an extensive bibliography. A thorough review of the requisite matrix algebra has been addedfor transitional purposes, and numerous theoretical and applied problems have been incorporated with selected answers provided at the end of the book. A related Web site includes additional data sets and SAS(r) code for all numerical examples.Linear Model in Statistics, Second Edition is a must-have book for courses in statistics, biostatistics, and mathematics at the upper-undergraduate and graduate levels. It is also an invaluable reference for researchers who need to gain a better understanding of regression and analysis of variance. Codice libro della libreria AAH9780471754985

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Alvin C. Rencher, G. Bruce Schaalje
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ISBN 10: 0471754986 ISBN 13: 9780471754985
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Descrizione libro John Wiley and Sons Ltd, United Kingdom, 2008. Hardback. Condizione libro: New. 2nd Revised edition. 239 x 160 mm. Language: English . Brand New Book. The essential introduction to the theory and application of linear models-now in a valuable new edition Since most advanced statistical tools are generalizations of the linear model, it is neces-sary to first master the linear model in order to move forward to more advanced concepts. The linear model remains the main tool of the applied statistician and is central to the training of any statistician regardless of whether the focus is applied or theoretical. This completely revised and updated new edition successfully develops the basic theory of linear models for regression, analysis of variance, analysis of covariance, and linear mixed models. Recent advances in the methodology related to linear mixed models, generalized linear models, and the Bayesian linear model are also addressed. Linear Models in Statistics, Second Edition includes full coverage of advanced topics, such as mixed and generalized linear models, Bayesian linear models, two-way models with empty cells, geometry of least squares, vector-matrix calculus, simultaneous inference, and logistic and nonlinear regression.Algebraic, geometrical, frequentist, and Bayesian approaches to both the inference of linear models and the analysis of variance are also illustrated. Through the expansion of relevant material and the inclusion of the latest technological developments in the field, this book provides readers with the theoretical foundation to correctly interpret computer software output as well as effectively use, customize, and understand linear models. This modern Second Edition features:* New chapters on Bayesian linear models as well as random and mixed linear models* Expanded discussion of two-way models with empty cells* Additional sections on the geometry of least squares* Updated coverage of simultaneous inference The book is complemented with easy-to-read proofs, real data sets, and an extensive bibliography. A thorough review of the requisite matrix algebra has been addedfor transitional purposes, and numerous theoretical and applied problems have been incorporated with selected answers provided at the end of the book. A related Web site includes additional data sets and SAS(r) code for all numerical examples.Linear Model in Statistics, Second Edition is a must-have book for courses in statistics, biostatistics, and mathematics at the upper-undergraduate and graduate levels. It is also an invaluable reference for researchers who need to gain a better understanding of regression and analysis of variance. Codice libro della libreria AAH9780471754985

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ISBN 10: 0471754986 ISBN 13: 9780471754985
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Descrizione libro 2008. HRD. Condizione libro: New. New Book. Shipped from US within 10 to 14 business days. Established seller since 2000. Codice libro della libreria KB-9780471754985

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Descrizione libro Wiley-Interscience, 2008. Hardcover. Condizione libro: New. Codice libro della libreria P110471754986

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