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Chapters

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- Acknowledgments
- Chapter 1 Introduction
- Chapter 2 Regression
- 2.1 Introduction
- 2.2 The REG Procedure
- 2.2.1 Using the REG Procedure to Fit a Model with One Independent Variable
- 2.2.2 The P, CLM, and CLI Options: Predicted Values and Confidence Limits
- 2.2.3 A Model with Several Independent Variables
- 2.2.4 The SS1 and SS2 Options: Two Types of Sums of Squares
- 2.2.5 Tests of Subsets and Linear Combinations of Coefficients
- 2.2.6 Fitting Restricted Models: The RESTRICT Statement and NOINT Option
- 2.2.7 Exact Linear Dependency

- 2.3 The GLM Procedure
- 2.4 Statistical Background

- Chapter 3 Analysis of Variance for Balanced Data
- 3.1 Introduction
- 3.2 One- and Two-Sample Tests and Statistics
- 3.3 The Comparison of Several Means: Analysis of Variance
- 3.4 The Analysis of One-Way Classification of Data
- 3.4.1 Computing the ANOVA Table
- 3.4.2 Computing Means, Multiple Comparisons of Means, and Confidence Intervals
- 3.4.3 Planned Comparisons for One-Way Classification: The CONTRAST Statement
- 3.4.4 Linear Combinations of Model Parameters
- 3.4.5 Testing Several Contrasts Simultaneously
- 3.4.6 Orthogonal Contrasts
- 3.4.7 Estimating Linear Combinations of Parameters: The ESTIMATE Statement

- 3.5 Randomized-Blocks Designs
- 3.6 A Latin Square Design with Two Response Variables
- 3.7 A Two-Way Factorial Experiment
- 3.7.1 ANOVA for a Two-Way Factorial Experiment
- 3.7.2 Multiple Comparisons for a Factorial Experiment
- 3.7.3 Multiple Comparisons of METHOD Means by VARIETY
- 3.7.4 Planned Comparisons in a Two-Way Factorial Experiment
- 3.7.5 Simple Effect Comparisons
- 3.7.6 Main Effect Comparisons
- 3.7.7 Simultaneous Contrasts in Two-Way Classifications
- 3.7.8 Comparing Levels of One Factor within Subgroups of Levels of Another Factor
- 3.7.9 An Easier Way to Set Up CONTRAST and ESTIMATE Statements

- Chapter 4 Analyzing Data with Random Effects
- 4.1 Introduction
- 4.2 Nested Classifications
- 4.2.1 Analysis of Variance for Nested Classifications
- 4.2.2 Computing Variances of Means from Nested Classifications and Deriving Optimum Sampling Plans
- 4.2.3 Analysis of Variance for Nested Classifications: Using Expected Mean Squares to Obtain Valid Tests of Hypotheses
- 4.2.4 Variance Component Estimation for Nested Classifications: Analysis Using PROC MIXED
- 4.2.5 Additional Analysis of Nested Classifications Using PROC MIXED: Overall Mean and Best Linear Unbiased Prediction

- 4.3 Blocked Designs with Random Blocks
- 4.4 The Two-Way Mixed Model
- 4.5 A Classification with Both Crossed and Nested Effects
- 4.6 Split-Plot Experiments

- Chapter 5 Unbalanced Data Analysis: Basic Methods
- 5.1 Introduction
- 5.2 Applied Concepts of Analyzing Unbalanced Data
- 5.3 Issues Associated with Empty Cells
- 5.4 Some Problems with Unbalanced Mixed-Model Data
- 5.5 Using the GLM Procedure to Analyze Unbalanced Mixed-Model Data
- 5.6 Using the MIXED Procedure to Analyze Unbalanced Mixed-Model Data
- 5.7 Using the GLM and MIXED Procedures to Analyze Mixed-Model Data with Empty Cells
- 5.8 Summary and Conclusions about Using the GLM and MIXED Procedures to Analyze Unbalanced Mixed-Model Data

- Chapter 6 Understanding Linear Models Concepts
- 6.1 Introduction
- 6.2 The Dummy-Variable Model
- 6.3 Two-Way Classification: Unbalanced Data
- 6.3.1 General Considerations
- 6.3.2 Sums of Squares Computed by PROC GLM
- 6.3.3 Interpreting Sums of Squares in Reduction Notation
- 6.3.4 Interpreting Sums of Squares in -Model Notation
- 6.3.5 An Example of Unbalanced Two-Way Classification
- 6.3.6 The MEANS, LSMEANS, CONTRAST, and ESTIMATE Statements in a Two-Way Layout
- 6.3.7 Estimable Functions for a Two-Way Classification
- 6.3.8 Empty Cells

- 6.4 Mixed-Model Issues
- 6.5 ANOVA Issues for Unbalanced Mixed Models
- 6.6 GLS and Likelihood Methodology Mixed Model

- Chapter 7 Analysis of Covariance
- Chapter 8 Repeated-Measures Analysis
- 8.1 Introduction
- 8.2 The Univariate ANOVA Method for Analyzing Repeated Measures
- 8.3 Multivariate and Univariate Methods Based on Contrasts of the Repeated Measures
- 8.4 Mixed-Model Analysis of Repeated Measures
- 8.4.1 The Fixed-Effects Model and Related Considerations
- 8.4.2 Selecting an Appropriate Covariance Model
- 8.4.3 Reassessing the Covariance Structure with a Means Model Accounting for Baseline Measurement
- 8.4.4 Information Criteria to Compare Covariance Models
- 8.4.5 PROC MIXED Analysis of FEV1 Data
- 8.4.6 Inference on the Treatment and Time Effects of FEV1 Data Using PROC MIXED

- Chapter 9 Multivariate Linear Models
- Chapter 10 Generalized Linear Models
- 10.1 Introduction
- 10.2 The Logistic and Probit Regression Models
- 10.3 Binomial Models for Analysis of Variance and Analysis of Covariance
- 10.4 Count Data and Overdispersion
- 10.4.1 An Insect Count Example
- 10.4.2 Model Checking
- 10.4.3 Correction for Overdispersion
- 10.4.4 Fitting a Negative Binomial Model
- 10.4.5 Using PROC GENMOD to Fit the Negative Binomial with a Log Link
- 10.4.6 Fitting the Negative Binomial with a Canonical Link
- 10.4.7 Advanced Application: A User-Supplied Program to Fit the Negative Binomial with a Canonical Link

- 10.5 Generalized Linear Models with Repeated Measures—Generalized Estimating Equations
- 10.6 Background Theory

- Chapter 11 Examples of Special Applications
- 11.1 Introduction
- 11.2 Confounding in a Factorial Experiment
- 11.3 A Balanced Incomplete-Blocks Design
- 11.4 A Crossover Design with Residual Effects
- 11.5 Models for Experiments with Qualitative and Quantitative Variables
- 11.6 A Lack-of-Fit Analysis
- 11.7 An Unbalanced Nested Structure
- 11.8 An Analysis of Multi-Location Data
- 11.9 Absorbing Nesting Effects

- References
- Index