Statistical Modelling in R
Research Methods
Statistical Modelling in R
Research Methods
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Description
An accessible introduction to statistical data analysis and modelling using the widely used programming language R. Beginning with a general introduction to the idea of statistical modelling, this book presents a range of approaches including linear regression models, models for binary, categorical and count data, and models for hierarchical and clustered data structures. It outlines various technical statistical aspects of these approaches, and their application in social science research. The final chapter is more practical and shows how readers can begin to undertake statistical modelling of their own. This book is for anyone working across the social sciences who is considering using statistical modelling in R in their own research.
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Table of Contents
Chapter 1. Introduction
Part 1
Chapter 2. General Linear Models
Chapter 3. Modelling Binary Outcomes
Chapter 4. Modelling Multiple Categorical Outcomes
Chapter 5. Modelling Ordered Categorical Outcomes
Chapter 6. Modelling Count Outcomes
Part 2
Chapter 7. Evaluating Statistical Models (diagnostics)
Chapter 8. Extending Statistical Models
Chapter 9. Missing Data
Chapter 10. The Workflow and Transparent and Reproducible Research
Chapter 11. Practical Advice for Statistical Modelling in R
Chapter 12. Further Adventures in Statistical Modelling
References
Index
Product details
| Published | 08 Jul 2027 |
|---|---|
| Format | Ebook (Epub & Mobi) |
| Edition | 1st |
| Pages | 144 |
| ISBN | 9781350139855 |
| Imprint | Bloomsbury Academic |
| Series | Bloomsbury Research Methods |
| Publisher | Bloomsbury Publishing |
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