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Linear Models with R Vital Source e-bog
Julian J. Faraway
(2025)
Linear Models with R
Julian J. Faraway
(2025)
Sprog: Engelsk
Detaljer om varen
- 3. Udgave
- Vital Source searchable e-book (Reflowable pages)
- Udgiver: Taylor & Francis (Marts 2025)
- ISBN: 9781040275863
Bookshelf online: 365 dage fra købsdato.
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Detaljer om varen
- 3. Udgave
- Hardback: 378 sider
- Udgiver: CRC Press LLC (Marts 2025)
- ISBN: 9781032583983
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.
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