A Practical Guide to Data Analysis Using R
Cambridge University Press (Verlag)
978-1-009-28227-7 (ISBN)
Using diverse real-world examples, this text examines what models used for data analysis mean in a specific research context. What assumptions underlie analyses, and how can you check them? Building on the successful 'Data Analysis and Graphics Using R,' 3rd edition (Cambridge, 2010), it expands upon topics including cluster analysis, exponential time series, matching, seasonality, and resampling approaches. An extended look at p-values leads to an exploration of replicability issues and of contexts where numerous p-values exist, including gene expression. Developing practical intuition, this book assists scientists in the analysis of their own data, and familiarizes students in statistical theory with practical data analysis. The worked examples and accompanying commentary teach readers to recognize when a method works and, more importantly, when it doesn't. Each chapter contains copious exercises. Selected solutions, notes, slides, and R code are available online, with extensive references pointing to detailed guides to R.
John H. Maindonald is Contract Associate at Statistics Research Associates and was previously Visiting Fellow at the Australian National University. He has had wide experience both as a university lecturer and as a quantitative problem solver, working with researchers in diverse areas. He is the author of 'Statistical Computation' (1984), and the senior author of 'Data Analysis and Graphics Using R' (third edition, 2010). W. John Braun is Professor at the University of British Columbia, where he is Director of the UBCO campus of the Banff International Research Station for Mathematical Innovation and Discovery. In 2020, he received the Statistical Society of Canada Award for Impact of Applied and Collaborative Work. Jeffrey Andrews is Associate Professor at the University of British Columbia. He currently serves as Principal Co-director of the Master of Data Science program and President-elect of The Classification Society (TCS). He is the 2013 Distinguished Dissertation Award winner from TCS and a recipient of the 2017 Chikio Hayashi Award for Young Researchers from the International Federation of Classification Societies.
1. Learning from data, and tools for the task; 2. Generalizing from models; 3. Multiple linear regression; 4. Exploiting the linear model framework; 5. Generalized linear models and survival analysis; 6. Time series models; 7. Multilevel models, and repeated measures; 8. Tree-based classification and regression; 9. Multivariate data exploration and discrimination; Epilogue; A. The R system – a brief overview; References; References to R packages; Index of R functions; Subject index.
| Erscheinungsdatum | 26.05.2024 |
|---|---|
| Zusatzinfo | Worked examples or Exercises |
| Verlagsort | Cambridge |
| Sprache | englisch |
| Maße | 175 x 251 mm |
| Gewicht | 1160 g |
| Themenwelt | Informatik ► Datenbanken ► Data Warehouse / Data Mining |
| Mathematik / Informatik ► Mathematik | |
| Naturwissenschaften ► Biologie | |
| ISBN-10 | 1-009-28227-1 / 1009282271 |
| ISBN-13 | 978-1-009-28227-7 / 9781009282277 |
| Zustand | Neuware |
| Haben Sie eine Frage zum Produkt? |
aus dem Bereich
