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
3 - Statistical models
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
Many regularities of nature are taken for granted in daily living – the rising and setting of the sun, the effects of fire in burning anyone unfortunate enough to get too near, and so on. Experience of the world, rather than logical deductive argument, has identified these regularities. Scientific investigation, especially in the physical sciences, has greatly extended and systematized awareness of regularities. Mathematical descriptions, i.e., models, have been crucial for describing and quantifying these regularities.
As when any model is pressed into service, it is important to understand which features generalize and which do not. An engineer's scale model of a building may be helpful for checking the routing of the plumbing but may give little indication of the acoustics of seminar rooms that are included in the building. In medical research, mouse responses to disease and to therapeutic agents are widely used as models for human responses. Experimental responses in the mouse may indicate likely responses in humans.
In fundamental research in the physical sciences, deterministic models are often adequate. Statistical variability may be so small that it can, for practical purposes, be ignored. In applications of the physical sciences, variability may more commonly be a serious issue. In studying how buildings respond to a demolition charge, there will be variation from one occasion to another, even for identical buildings and identically placed charges. There will be variation in which parts of the building break first, in what parts remain intact, and in the trajectories of fragments.
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- Information
- Data Analysis and Graphics Using RAn Example-Based Approach, pp. 77 - 101Publisher: Cambridge University PressPrint publication year: 2010