Data Analysis and Graphics Using R
An Example-Based Approach
Nonfiction, Computers, Advanced Computing, Natural Language Processing, Science & Nature, Mathematics, Health & Well Being, Medical
| Author: | John Maindonald, W. John Braun | ISBN: | 9781107385481 |
| Publisher: | Cambridge University Press | Publication: | May 6, 2010 |
| Imprint: | Cambridge University Press | Language: | English |
| Author: | John Maindonald, W. John Braun |
| ISBN: | 9781107385481 |
| Publisher: | Cambridge University Press |
| Publication: | May 6, 2010 |
| Imprint: | Cambridge University Press |
| Language: | English |
Discover what you can do with R! Introducing the R system, covering standard regression methods, then tackling more advanced topics, this book guides users through the practical, powerful tools that the R system provides. The emphasis is on hands-on analysis, graphical display, and interpretation of data. The many worked examples, from real-world research, are accompanied by commentary on what is done and why. The companion website has code and datasets, allowing readers to reproduce all analyses, along with solutions to selected exercises and updates. Assuming basic statistical knowledge and some experience with data analysis (but not R), the book is ideal for research scientists, final-year undergraduate or graduate-level students of applied statistics, and practising statisticians. It is both for learning and for reference. This third edition expands upon topics such as Bayesian inference for regression, errors in variables, generalized linear mixed models, and random forests.
Discover what you can do with R! Introducing the R system, covering standard regression methods, then tackling more advanced topics, this book guides users through the practical, powerful tools that the R system provides. The emphasis is on hands-on analysis, graphical display, and interpretation of data. The many worked examples, from real-world research, are accompanied by commentary on what is done and why. The companion website has code and datasets, allowing readers to reproduce all analyses, along with solutions to selected exercises and updates. Assuming basic statistical knowledge and some experience with data analysis (but not R), the book is ideal for research scientists, final-year undergraduate or graduate-level students of applied statistics, and practising statisticians. It is both for learning and for reference. This third edition expands upon topics such as Bayesian inference for regression, errors in variables, generalized linear mixed models, and random forests.
