Book contents
- Frontmatter
- Dedication
- Contents
- Preface
- Content-how the chapters fit together
- 1 A brief introduction to R
- 2 Styles of data analysis
- 3 Statistical models
- 4 A review of inference concepts
- 5 Regression with a single predictor
- 6 Multiple linear regression
- 7 Exploiting the linear model framework
- 8 Generalized linear models and survival analysis
- 9 Time series models
- 10 Multi-level models and repeated measures
- 11 Tree-based classification and regression
- 12 Multivariate data exploration and discrimination
- 13 Regression on principal component or discriminant scores
- 14 The R system – additional topics
- 15 Graphs in R
- Epilogue
- References
- Index of R symbols and functions
- Index of terms
- Index of authors
- Plate Section
Epilogue
Published online by Cambridge University Press: 05 October 2013
- Frontmatter
- Dedication
- Contents
- Preface
- Content-how the chapters fit together
- 1 A brief introduction to R
- 2 Styles of data analysis
- 3 Statistical models
- 4 A review of inference concepts
- 5 Regression with a single predictor
- 6 Multiple linear regression
- 7 Exploiting the linear model framework
- 8 Generalized linear models and survival analysis
- 9 Time series models
- 10 Multi-level models and repeated measures
- 11 Tree-based classification and regression
- 12 Multivariate data exploration and discrimination
- 13 Regression on principal component or discriminant scores
- 14 The R system – additional topics
- 15 Graphs in R
- Epilogue
- References
- Index of R symbols and functions
- Index of terms
- Index of authors
- Plate Section
Summary
The first several chapters of this book were introductory in style, though with more attention to the subtleties of practical application than is common in elementary treatments. The simple models considered here are somewhat restricted in their range of application, because they are based on assumptions of independence and stationarity which will not nearly always be true. The last several chapters treated topics that have traditionally been thought of as more advanced. We hope this has given readers a taste of models for data which can be applied in a wide variety of situations.
Models that are not strictly correct, or even perhaps badly broken, may nevertheless be useful, giving acceptably accurate predictions. The validity of model assumptions remains an important issue. We need to know the limits of the usefulness of our models. Comparing results from a simple model with results from a model that takes better account of the data can help in developing intuition. Somewhat ironically, Chapters 7-13 could be viewed as essential background for those who hope to do a good job of the analyses described in Chapters 3-6! An understanding of multi-level and repeated measures models seems particularly important, since these models bring attention to structure in the noise component which must be accounted for in a proper analysis.
- Type
- Chapter
- Information
- Data Analysis and Graphics Using RAn Example-Based Approach, pp. 493 - 494Publisher: Cambridge University PressPrint publication year: 2010