Regression and ANOVA: An Integrated Approach Using SAS Software von Muller, Keith: new Paperback (2012)

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Bibliografische Details

Titel: Regression and ANOVA: An Integrated Approach Using SAS Software
Verlag: SAS Institute
Erscheinungsjahr: 2012
Sprache: Englisch
ISBN-10: 1580258905
ISBN-13: 9781580258906
Einband: Paperback
Zustand: new

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Inhaltsangabe

Regression and ANOVA: An Integrated Approach Using SAS Software, by Keith Muller and Bethel Fetterman, provides a thorough and integrated treatment of multiple regression and ANOVA. The information it contains has served as the basis for a graduate-level biostatistics class at the University of North Carolina at Chapel Hill. The book focuses on the general linear model (GLM) theory, stated in matrix terms, which provides a more compact, clear, and unified presentation of regression and ANOVA than do traditional sums of squares and scalar equations. The book offers a balanced treatment of regression and ANOVA, yet is very compact. Reflecting current computational practice, most sums of squares formulas and associated theory, especially in ANOVA, are not included. The text includes almost no proofs, despite the presence of a large number of basic theoretical results. Many numerical examples are provided and include both the SAS code and equivalent mathematical representation needed to produce the outputs that are presented. All exercises involve only real data, collected in the course of scientific research. The book is divided into sections covering the following topics: basic theory; multiple regression; model building and evaluation; ANOVA; and ANCOVA.

Rese�a del editor

Regression and ANOVA: An Integrated Approach Using SAS Software, by Keith Muller and Bethel Fetterman, provides a thorough and integrated treatment of multiple regression and ANOVA. The information it contains has served as the basis for a graduate-level biostatistics class at the University of North Carolina at Chapel Hill. The book focuses on the general linear model (GLM) theory, stated in matrix terms, which provides a more compact, clear, and unified presentation of regression and ANOVA than do traditional sums of squares and scalar equations. The book offers a balanced treatment of regression and ANOVA, yet is very compact. Reflecting current computational practice, most sums of squares formulas and associated theory, especially in ANOVA, are not included. The text includes almost no proofs, despite the presence of a large number of basic theoretical results. Many numerical examples are provided and include both the SAS code and equivalent mathematical representation needed to produce the outputs that are presented. All exercises involve only real data, collected in the course of scientific research. The book is divided into sections covering the following topics: basic theory; multiple regression; model building and evaluation; ANOVA; and ANCOVA.

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