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Edition: 3RD 02

Copyright: 2002

Publisher: Springer-Verlag New York

Published: 2002

International: No

Copyright: 2002

Publisher: Springer-Verlag New York

Published: 2002

International: No

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This textbook provides a wide-ranging introduction to the use and theory of linear models for analyzing data. The author's emphasis is on providing a unified treatment of linear models, including analysis of variance models and regression models, based on projections, orthogonality, and other vector space ideas. Every chapter comes with numerous exercises and examples that make it ideal for a graduate-level course.

All of the standard topics are covered in depth: ANOVA, estimation including Bayesian estimation, hypothesis testing, multiple comparisons, regression analysis, and experimental design models. In addition, the book covers topics that are not usually treated at this level, but which are important in their own right: balanced incomplete block designs, testing for lack of fit, testing for independence, models with singular covariance matrices, variance component estimation, best linear and best linear unbiased prediction, collinearity, and variable selection.

This new edition includes discussion of identifiability and its relationship to estimability, different approaches to the theories of testing parametric hypotheses and analysis of covariance, additional discussion of the geometry of least squares estimation and testing, new discussion of models for experiments with factorial treatment structures, and a new appendix on possible causes for getting test statistics that are so small as to be suspicious.

**Christensen, Ronald : University of New Mexico, Albuquerque**

Ronald Christensen is a Professor of Statistics at the University of New Mexico. He is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics.

Introduction

Estimation

Testing Hypotheses

One-Way ANOVA

Multiple Comparison Techniques

Regression Analysis

Multifactor Analysis of Variance

Experimental Design Models

Analysis of Covariance

Estimation and Testing in General Gauss-Markov Models

Split Plot Models

Mixed Models and Variance Components

Checking Assumptions, Residuals, and Influential Observations

Variable Selection and Collinearity

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Summary

This textbook provides a wide-ranging introduction to the use and theory of linear models for analyzing data. The author's emphasis is on providing a unified treatment of linear models, including analysis of variance models and regression models, based on projections, orthogonality, and other vector space ideas. Every chapter comes with numerous exercises and examples that make it ideal for a graduate-level course.

All of the standard topics are covered in depth: ANOVA, estimation including Bayesian estimation, hypothesis testing, multiple comparisons, regression analysis, and experimental design models. In addition, the book covers topics that are not usually treated at this level, but which are important in their own right: balanced incomplete block designs, testing for lack of fit, testing for independence, models with singular covariance matrices, variance component estimation, best linear and best linear unbiased prediction, collinearity, and variable selection.

This new edition includes discussion of identifiability and its relationship to estimability, different approaches to the theories of testing parametric hypotheses and analysis of covariance, additional discussion of the geometry of least squares estimation and testing, new discussion of models for experiments with factorial treatment structures, and a new appendix on possible causes for getting test statistics that are so small as to be suspicious.

Author Bio

**Christensen, Ronald : University of New Mexico, Albuquerque**

Ronald Christensen is a Professor of Statistics at the University of New Mexico. He is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics.

Table of Contents

Introduction

Estimation

Testing Hypotheses

One-Way ANOVA

Multiple Comparison Techniques

Regression Analysis

Multifactor Analysis of Variance

Experimental Design Models

Analysis of Covariance

Estimation and Testing in General Gauss-Markov Models

Split Plot Models

Mixed Models and Variance Components

Checking Assumptions, Residuals, and Influential Observations

Variable Selection and Collinearity

Publisher Info

Publisher: Springer-Verlag New York

Published: 2002

International: No

Published: 2002

International: No

This textbook provides a wide-ranging introduction to the use and theory of linear models for analyzing data. The author's emphasis is on providing a unified treatment of linear models, including analysis of variance models and regression models, based on projections, orthogonality, and other vector space ideas. Every chapter comes with numerous exercises and examples that make it ideal for a graduate-level course.

All of the standard topics are covered in depth: ANOVA, estimation including Bayesian estimation, hypothesis testing, multiple comparisons, regression analysis, and experimental design models. In addition, the book covers topics that are not usually treated at this level, but which are important in their own right: balanced incomplete block designs, testing for lack of fit, testing for independence, models with singular covariance matrices, variance component estimation, best linear and best linear unbiased prediction, collinearity, and variable selection.

This new edition includes discussion of identifiability and its relationship to estimability, different approaches to the theories of testing parametric hypotheses and analysis of covariance, additional discussion of the geometry of least squares estimation and testing, new discussion of models for experiments with factorial treatment structures, and a new appendix on possible causes for getting test statistics that are so small as to be suspicious.

**Christensen, Ronald : University of New Mexico, Albuquerque**

Ronald Christensen is a Professor of Statistics at the University of New Mexico. He is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics.

Estimation

Testing Hypotheses

One-Way ANOVA

Multiple Comparison Techniques

Regression Analysis

Multifactor Analysis of Variance

Experimental Design Models

Analysis of Covariance

Estimation and Testing in General Gauss-Markov Models

Split Plot Models

Mixed Models and Variance Components

Checking Assumptions, Residuals, and Influential Observations

Variable Selection and Collinearity