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by Neter, Kutner and Nachtsheim

Edition: 5TH 05Copyright: 2005

Publisher: Richard D. Irwin, Inc.

Published: 2005

International: No

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Applied Linear Statistical Models 5e is the long established leading authoritative text and reference on statistical modeling, analysis of variance, and the design of experiments. For students in most any discipline where statistical analysis or interpretation is used, ALSM serves as the standard work. The text proceeds through linear and nonlinear regression and modeling for the first half, and through ANOVA and Experimental Design in the second half. All topics are presented in a precise and clear style supported with solved examples, numbered formulae, graphic illustrations, and "Comments" to provide depth and statistical accuracy and precision. Applications used within the text and the hallmark problems, exercises, projects, and case studies are drawn from virtually all disciplines and fields providing motivation for students in virtually any college. The Fifth edition provides an increased use of computing and graphical analysis throughout, without sacrificing concepts or rigor. In general, the 5e uses larger data sets in examples and exercises, and the use of automated software without loss of understanding.

**New to This Edition**

- Updated throughout to include the latest developments and methods in statistics such as advanced bootstrapping, neural networks, regression trees, other blocking approaches, Taguchi Methodology, and more.
- Extensively revised coverage of logistic regression, including polytomous nominal and ordinal logistic regression models.
- Expanded discussion of model selection methods and criteria including Akaike Information Criterion and the Schwarz Bayesian Criterion.
- A new Chapter 15, Introduction to the Design of Experiments and Observational Studies provides a basic framework for the design and analysis of scientific studies.
- The flow has been improved throughout, combining related chapters and smoothing transitions between concepts. Overall, the text has been reduced from 32 chapters to 30 with one entirely new chapter.
- New open ended 'Cases' based on data sets from business, health care, and engineering are included. Also, many problem data sets have been updated and expanded.
- The text includes a CD with all data sets and the Student Solutions manual in PDF. In addition a new supplement, SAS and SPSS Program Solutions by Replogle and Johnson is available for the Fifth Edition.

**Features**

- Applied Linear Statistical Models contains all chapters in the Applied Linear Regression Models Fourth Edition, plus an additional 16 chapters on single and multifactor ANOVA and Experimental Design.
- The standard regression framework is emphasized throughout the text.

**Part 1 Simple Linear Regression**

1 Linear Regression with One Predictor Variable

2 Inferences in Regression and Correlation Analysis

3 Diagnostic and Remedial Measures

4 Simultaneous Inferences and Other Topics in Regression Analysis

5 Matrix Approach to Simple Linear Regression Analysis

**Part 2 Multiple Linear Regression**

6 Multiple Regression I

7 Multiple Regression II

8 Regression Models for Quantitative and Qualitative Predictors

9 Building the Regression Model I: Model Selection and Validation

10 Building the Regression Model II: Diagnostics

11 Building the Regression Model III: Remedial Measures

12 Autocorrelation in Time Series Data

**Part 3 Nonlinear Regression**

13 Introduction to Nonlinear Regression and Neural Networks

14 Logistic Regression, Poisson Regression, and Generalized Linear Models

**Part 4 Design and Analysis of Single-Factor Studies**

15 Introduction to the Design of Experimental and Observational Studies

16 Single Factor Studies

17 Analysis of Factor-Level Means

18 ANOVA Diagnostics and Remedial Measures

**Part 5 Multi-Factor Studies**

19 Two Factor Studies with Equal Sample Sizes

20 Two Factor Studies-One Case per Treatment

21 Randomized Complete Block Designs

22 Analysis of Covariance

23 Two Factor Studies with Unequal Sample Sizes

24 MultiFactor Studies

25 Random and Mixed Effects Models

**Part 6 Specialized Study Designs**

26 Nested Designs, Subsampling, and Partially Nested Designs

27 Repeated Measures and Related Designs

28 Balanced Incomplete Block, Latin Square, and Related Designs

29 Exploratory Experiments: Two-Level Factorial and Fractional Factorial Designs

30 Response Surface Methodology

Appendix A: Some Basic Results in Probability and Statistics

Appendix B: Tables

Appendix C: Data Sets

Appendix D: Rules for Develping ANOVA Models and Tables for Balanced Designs

Appendix E: Selected Bibliography

Summary

Applied Linear Statistical Models 5e is the long established leading authoritative text and reference on statistical modeling, analysis of variance, and the design of experiments. For students in most any discipline where statistical analysis or interpretation is used, ALSM serves as the standard work. The text proceeds through linear and nonlinear regression and modeling for the first half, and through ANOVA and Experimental Design in the second half. All topics are presented in a precise and clear style supported with solved examples, numbered formulae, graphic illustrations, and "Comments" to provide depth and statistical accuracy and precision. Applications used within the text and the hallmark problems, exercises, projects, and case studies are drawn from virtually all disciplines and fields providing motivation for students in virtually any college. The Fifth edition provides an increased use of computing and graphical analysis throughout, without sacrificing concepts or rigor. In general, the 5e uses larger data sets in examples and exercises, and the use of automated software without loss of understanding.

**New to This Edition**

- Updated throughout to include the latest developments and methods in statistics such as advanced bootstrapping, neural networks, regression trees, other blocking approaches, Taguchi Methodology, and more.
- Extensively revised coverage of logistic regression, including polytomous nominal and ordinal logistic regression models.
- Expanded discussion of model selection methods and criteria including Akaike Information Criterion and the Schwarz Bayesian Criterion.
- A new Chapter 15, Introduction to the Design of Experiments and Observational Studies provides a basic framework for the design and analysis of scientific studies.
- The flow has been improved throughout, combining related chapters and smoothing transitions between concepts. Overall, the text has been reduced from 32 chapters to 30 with one entirely new chapter.
- New open ended 'Cases' based on data sets from business, health care, and engineering are included. Also, many problem data sets have been updated and expanded.
- The text includes a CD with all data sets and the Student Solutions manual in PDF. In addition a new supplement, SAS and SPSS Program Solutions by Replogle and Johnson is available for the Fifth Edition.

**Features**

- Applied Linear Statistical Models contains all chapters in the Applied Linear Regression Models Fourth Edition, plus an additional 16 chapters on single and multifactor ANOVA and Experimental Design.
- The standard regression framework is emphasized throughout the text.

Table of Contents

**Part 1 Simple Linear Regression**

1 Linear Regression with One Predictor Variable

2 Inferences in Regression and Correlation Analysis

3 Diagnostic and Remedial Measures

4 Simultaneous Inferences and Other Topics in Regression Analysis

5 Matrix Approach to Simple Linear Regression Analysis

**Part 2 Multiple Linear Regression**

6 Multiple Regression I

7 Multiple Regression II

8 Regression Models for Quantitative and Qualitative Predictors

9 Building the Regression Model I: Model Selection and Validation

10 Building the Regression Model II: Diagnostics

11 Building the Regression Model III: Remedial Measures

12 Autocorrelation in Time Series Data

**Part 3 Nonlinear Regression**

13 Introduction to Nonlinear Regression and Neural Networks

14 Logistic Regression, Poisson Regression, and Generalized Linear Models

**Part 4 Design and Analysis of Single-Factor Studies**

15 Introduction to the Design of Experimental and Observational Studies

16 Single Factor Studies

17 Analysis of Factor-Level Means

18 ANOVA Diagnostics and Remedial Measures

**Part 5 Multi-Factor Studies**

19 Two Factor Studies with Equal Sample Sizes

20 Two Factor Studies-One Case per Treatment

21 Randomized Complete Block Designs

22 Analysis of Covariance

23 Two Factor Studies with Unequal Sample Sizes

24 MultiFactor Studies

25 Random and Mixed Effects Models

**Part 6 Specialized Study Designs**

26 Nested Designs, Subsampling, and Partially Nested Designs

27 Repeated Measures and Related Designs

28 Balanced Incomplete Block, Latin Square, and Related Designs

29 Exploratory Experiments: Two-Level Factorial and Fractional Factorial Designs

30 Response Surface Methodology

Appendix A: Some Basic Results in Probability and Statistics

Appendix B: Tables

Appendix C: Data Sets

Appendix D: Rules for Develping ANOVA Models and Tables for Balanced Designs

Appendix E: Selected Bibliography

Publisher Info

Publisher: Richard D. Irwin, Inc.

Published: 2005

International: No

Published: 2005

International: No

Applied Linear Statistical Models 5e is the long established leading authoritative text and reference on statistical modeling, analysis of variance, and the design of experiments. For students in most any discipline where statistical analysis or interpretation is used, ALSM serves as the standard work. The text proceeds through linear and nonlinear regression and modeling for the first half, and through ANOVA and Experimental Design in the second half. All topics are presented in a precise and clear style supported with solved examples, numbered formulae, graphic illustrations, and "Comments" to provide depth and statistical accuracy and precision. Applications used within the text and the hallmark problems, exercises, projects, and case studies are drawn from virtually all disciplines and fields providing motivation for students in virtually any college. The Fifth edition provides an increased use of computing and graphical analysis throughout, without sacrificing concepts or rigor. In general, the 5e uses larger data sets in examples and exercises, and the use of automated software without loss of understanding.

**New to This Edition**

- Updated throughout to include the latest developments and methods in statistics such as advanced bootstrapping, neural networks, regression trees, other blocking approaches, Taguchi Methodology, and more.
- Extensively revised coverage of logistic regression, including polytomous nominal and ordinal logistic regression models.
- Expanded discussion of model selection methods and criteria including Akaike Information Criterion and the Schwarz Bayesian Criterion.
- A new Chapter 15, Introduction to the Design of Experiments and Observational Studies provides a basic framework for the design and analysis of scientific studies.
- The flow has been improved throughout, combining related chapters and smoothing transitions between concepts. Overall, the text has been reduced from 32 chapters to 30 with one entirely new chapter.
- New open ended 'Cases' based on data sets from business, health care, and engineering are included. Also, many problem data sets have been updated and expanded.
- The text includes a CD with all data sets and the Student Solutions manual in PDF. In addition a new supplement, SAS and SPSS Program Solutions by Replogle and Johnson is available for the Fifth Edition.

**Features**

- Applied Linear Statistical Models contains all chapters in the Applied Linear Regression Models Fourth Edition, plus an additional 16 chapters on single and multifactor ANOVA and Experimental Design.
- The standard regression framework is emphasized throughout the text.

**Part 1 Simple Linear Regression**

1 Linear Regression with One Predictor Variable

2 Inferences in Regression and Correlation Analysis

3 Diagnostic and Remedial Measures

4 Simultaneous Inferences and Other Topics in Regression Analysis

5 Matrix Approach to Simple Linear Regression Analysis

**Part 2 Multiple Linear Regression**

6 Multiple Regression I

7 Multiple Regression II

8 Regression Models for Quantitative and Qualitative Predictors

9 Building the Regression Model I: Model Selection and Validation

10 Building the Regression Model II: Diagnostics

11 Building the Regression Model III: Remedial Measures

12 Autocorrelation in Time Series Data

**Part 3 Nonlinear Regression**

13 Introduction to Nonlinear Regression and Neural Networks

14 Logistic Regression, Poisson Regression, and Generalized Linear Models

**Part 4 Design and Analysis of Single-Factor Studies**

15 Introduction to the Design of Experimental and Observational Studies

16 Single Factor Studies

17 Analysis of Factor-Level Means

18 ANOVA Diagnostics and Remedial Measures

**Part 5 Multi-Factor Studies**

19 Two Factor Studies with Equal Sample Sizes

20 Two Factor Studies-One Case per Treatment

21 Randomized Complete Block Designs

22 Analysis of Covariance

23 Two Factor Studies with Unequal Sample Sizes

24 MultiFactor Studies

25 Random and Mixed Effects Models

**Part 6 Specialized Study Designs**

26 Nested Designs, Subsampling, and Partially Nested Designs

27 Repeated Measures and Related Designs

28 Balanced Incomplete Block, Latin Square, and Related Designs

29 Exploratory Experiments: Two-Level Factorial and Fractional Factorial Designs

30 Response Surface Methodology

Appendix A: Some Basic Results in Probability and Statistics

Appendix B: Tables

Appendix C: Data Sets

Appendix D: Rules for Develping ANOVA Models and Tables for Balanced Designs

Appendix E: Selected Bibliography