Linear Models with R
eBook - ePub

Linear Models with R

  1. 392 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Linear Models with R

About this book

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.

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Yes, you can access Linear Models with R by Julian J. Faraway in PDF and/or ePUB format. We have over one million books available in our catalogue for you to explore.

Information

Table of contents

  1. Cover Page
  2. Half-Title Page
  3. Series Page
  4. Title Page
  5. Copyright Page
  6. Contents
  7. Preface
  8. 1 Introduction
  9. 2 Estimation
  10. 3 Inference
  11. 4 Sampling
  12. 5 Prediction
  13. 6 Explanation and Causation
  14. 7 Diagnostics
  15. 8 Predictor Issues
  16. 9 Modeling with the Error
  17. 10 Transformation
  18. 11 Model Selection
  19. 12 Regularization
  20. 13 Insurance Redlining — A Complete Example
  21. 14 Missing Data
  22. 15 Categorical Predictors
  23. 16 One Factor Models
  24. 17 Models with Several Factors
  25. 18 Experiments with Blocks
  26. Appendix
  27. Bibliography
  28. Index