Applied Linear Statistical Models – Michael Kutner – 5th Edition

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

29Exploratory 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