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
2 - Styles of data analysis
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
What is the best way to begin investigation of a new set of data? What forms of data exploration will draw attention to obvious errors or quirks in the data, or to obvious clues that the data contain? What checks are desirable before proceeding with an intended formal analysis, or to help decide what formal analysis may be appropriate? What can be learned from investigations that other researchers have done with similar data?
Competent statisticians have always used graphs to check their data. Numerical summaries, such as an average, can be very useful, but important features of the data may be missed without a glance at an appropriate graph. Careful consideration may be needed to choose a graph that will be effective for the purpose in hand.
We will see in Chapter 3 that an integral part of statistical analysis is the development of a model that accurately describes the data, clarifies what the data say, and makes prediction possible. Without model assumptions, there cannot be a meaningful formal analysis! As assumptions are strengthened, the chances of getting clear results improve. The price for stronger assumptions is that, if wrong, the results may be wrong. Graphical techniques have been developed for checking, to the extent possible, many of the assumptions that must be made in practice.
- Type
- Chapter
- Information
- Data Analysis and Graphics Using RAn Example-Based Approach, pp. 43 - 76Publisher: Cambridge University PressPrint publication year: 2010