Chapter 1

• Text Examples 1.1+ — Data List; SLR with Predicted Values and Residuals; Data and Regression Plot
• Text Examples 1.1+ — SLR with Predicted Values, Residuals, Confidence Intervals, Prediction Intervals; Regression Plot with Confidence Bounds
• Text Example 1.12 — Regression Through the Origin

Chapter 2

• Text Examples 2.1, 2.2, 2.3, 2.4, 2.10 — Various Matrix Operations

Chapter 3

• Text Examples 3.1+ — Simple Linear Regression using Matrix Operations

Chapter 4

• Text Examples 4.2-4.5 — Sums of Squares and Variance Estimates
• Physical Fitness Data (needed for the next two examples)
• Text Example 4.8 — Tests of General Linear Hypotheses
• Text Example 4.10 — Sequential and Partial Sums of Squares

Chapter 5

• Linthurst Data (Spartina Biomass)
• Case Study (Spartina Biomass)

• Linthurst Data (Spartina Biomass)
• Text Examples 7.1-7.7 — Variable Selection

• Text Example 9.2 — Class Variable Parameterizations
• Text Example 9.4 — Homogeneity of Regressions
• Companies Data
• Companies Example — Homogeneity of Regressions
  • Regressions Plot

• Text Example 8.2 — Univariate Polynomial Regression
• Text Example 8.3 — Lack of Fit for Polynomial Regression; Quadratic Regression Plot
• Text Example 8.4 — Orthogonal Polynomials
• Text Section 8.2 — Trigonometric Regression and Plot
• Salmon Data
• Text Example 8.5 — Quadratic Response Surfaces (Salmon Data)

• Linthurst Data (Spartina Biomass)
• Text Example 11.17 — Linthurst Data Diagnostics

• Text Example 12.2 — Variance-Stabilizing Transformation for Poisson (Simulated Data)
• Watershed Data
• Text Exercise 12.7 — Box-Cox Transformation (Watershed Data)
• Highway Fatalities Example — Generalized Least Squares with Estimated First-Order Autoregressive Model

• Groundhog Day Data
• Groundhog Day Example — Logistic Regression

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