Linear Models with R

Auteur

Julian J. Faraway is a professor of statistics in the Department of Mathematical Sciences at the University of Bath. He is an applied statistician with particular application to human motion, air pollution, anxiety and depression, astronomy, cleft lip and palate, flooding, fungicides, fuel filters, marketing, obesity and wastewater-based epidemiology. He earned a PhD in statistics from the University of California, Berkeley.

Texte du rabat

Linear Models with R, Third Edition explains how to use linear models in physical science, engineering, social science, and business applications. The book incorporates several improvements that reflect how the world of R has expanded since the publication of the second edition.

Résumé

A Hands-On Way to Learning Data Analysis

Part of the core of statistics, linear models are used to make predictions and explain the relationship between the response and the predictors. Understanding linear models is crucial to a broader competence in the practice of statistics. Linear Models with R, Third Edition explains how to use linear models in physical science, engineering, social science, and business applications. The book incorporates several improvements that reflect how the world of R has greatly expanded since the publication of the second edition.

New to the Third Edition

• 40% more content with more explanation and examples throughout
• New chapter on sampling featuring simulation-based methods
• Model assessment methods discussed
• Explanation chapter expanded to include introductory ideas about causation
• Model interpretation in the presence of transformation
• Crossvalidation for model selection
• Chapter on regularization now includes the elastic net
• More on multiple comparisons and the use of marginal means
• Discussion of design and power Like its widely praised, best-selling predecessor, this edition combines statistics and R to seamlessly give a coherent exposition of the practice of linear modeling. The text offers up-to-date insight on essential data analysis topics, from estimation, inference, and prediction to missing data, factorial models, and block designs. Numerous examples illustrate how to apply the different methods using R.

Contenu

Preface 1. Introduction 2. Estimation 3. Inference 4. Sampling 5. Prediction 6. Explanation and Causation 7. Diagnostics 8. Predictor issues 9. Modeling with the Error 10. Transformation 11. Model Selection 12. Regularization 13. Insurance Redlining - A Complete Example 14. Missing Data 15. Categorical Predictors 16. One Factor Models 17. Models with Several Factors 18. Experiments with Blocks Appendix A. About R Bibliography Index