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