faraway

Table of contents and R scripts

Click the heading to get individual scripts or download a zip file of all the scripts

  1. Introduction
    • Before You Start
    • Initial Data Analysis
    • When to Use Linear Modeling
    • History
  2. Estimation
    • Linear Model
    • Matrix Representation
    • Estimating β
    • Least Squares Estimation
    • Examples of Calculating β
    • Example
    • QR Decomposition
    • Gauss–Markov Theorem
    • Goodness of Fit
    • Identifiability
    • Orthogonality
  3. Inference
    • Hypothesis Tests to Compare Models
    • Testing Examples
    • Permutation Tests
    • Sampling
    • Confidence Intervals for β
    • Bootstrap Confidence Intervals
  4. Prediction
    • Confidence Intervals for Predictions
    • Predicting Body Fat
    • Autoregression
    • What Can Go Wrong with Predictions?
  5. Explanation
    • Simple Meaning
    • Causality
    • Designed Experiments
    • Observational Data
    • Matching
    • Covariate Adjustment
    • Qualitative Support for Causation
  6. Diagnostics
    • Checking Error Assumptions
    • Constant Variance
    • Normality
    • Correlated Errors - Finding Unusual Observations
    • Leverage
    • Outliers
    • Influential Observations - Checking the Structure of the Model - Discussion
  7. Problems with the Predictors
    • Errors in the Predictors
    • Changes of Scale
    • Collinearity
  8. Problems with the Error
    • Generalized Least Squares
    • Weighted Least Squares
    • Testing for Lack of Fit
    • Robust Regression
    • M-Estimation
    • Least Trimmed Squares
  9. Transformation
    • Transforming the Response
    • Transforming the Predictors
    • Broken Stick Regression
    • Polynomials
    • Splines
    • Additive Models
    • More Complex Models
  10. Model Selection
    • Hierarchical Models
    • Testing-Based Procedures
    • Criterion-Based Procedures
    • Summary
  11. Shrinkage Methods
    • Principal Components
    • Partial Least Squares
    • Ridge Regression
    • Lasso
  12. Insurance Redlining — A Complete Example
    • Ecological Correlation
    • Initial Data Analysis
    • Full Model and Diagnostics
    • Sensitivity Analysis
    • Discussion
  13. Missing Data
    • Types of Missing Data
    • Deletion
    • Single Imputation
    • Multiple Imputation
  14. Categorical Predictors
    • A Two-Level Factor
    • Factors and Quantitative Predictors
    • Interpretation with Interaction Terms
    • Factors With More Than Two Levels
    • Alternative Codings of Qualitative Predictors
  15. One Factor Models
    • The Model
    • An Example
    • Diagnostics
    • Pairwise Comparisons
    • False Discovery Rate
  16. Models with Several Factors
    • Two Factors with No Replication
    • Two Factors with Replication
    • Two Factors with an Interaction
    • Larger Factorial Experiments
  17. Experiments with Blocks
    • Randomized Block Design
    • Latin Squares
    • Balanced Incomplete Block Design
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