Linear Models with R
Chapman & Hall/CRC (Verlag)
978-1-032-58398-3 (ISBN)
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.
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.
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
| Erscheinungsdatum | 28.03.2025 |
|---|---|
| Reihe/Serie | Chapman & Hall/CRC Texts in Statistical Science |
| Zusatzinfo | 5 Tables, black and white; 102 Line drawings, black and white; 102 Illustrations, black and white |
| Sprache | englisch |
| Maße | 156 x 234 mm |
| Gewicht | 880 g |
| Themenwelt | Mathematik / Informatik ► Mathematik |
| Naturwissenschaften ► Biologie | |
| ISBN-10 | 1-032-58398-3 / 1032583983 |
| ISBN-13 | 978-1-032-58398-3 / 9781032583983 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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