Modern Statistics with R
Chapman & Hall/CRC (Verlag)
978-1-032-51244-0 (ISBN)
Data wrangling – importing, formatting, reshaping, merging, and filtering data in R.
Exploratory data analysis – using visualisations and multivariate techniques to explore datasets.
Statistical inference – modern methods for testing hypotheses and computing confidence intervals.
Predictive modelling – regression models and machine learning methods for prediction, classification, and forecasting.
Simulation – using simulation techniques for sample size computations and evaluations of statistical methods.
Ethics in statistics – ethical issues and good statistical practice.
R programming – writing code that is fast, readable, and (hopefully!) free from bugs.
No prior programming experience is necessary. Clear explanations and examples are provided to accommodate readers at all levels of familiarity with statistical principles and coding practices. A basic understanding of probability theory can enhance comprehension of certain concepts discussed within this book.
In addition to plenty of examples, the book includes more than 200 exercises, with fully worked solutions available at: www.modernstatisticswithr.com.
Måns Thulin is a consultant, researcher, and teacher in Statistics. He started teaching Statistics with R at Uppsala University in 2007, while still an undergraduate student. Since then, he has used Statistics and R to tackle problems in diverse fields, ranging from how to identify nuclear fuel and how to improve milking robots, to wine tastings and designing music videos. His awardwinning research in Statistical Methodology is concerned with modern approaches to classical statistical methods.
1. Introduction 2. The basics 3. The cornerstones of statistics 4. Exploratory data analysis and unsupervised learning 5. Dealing with messy data 6. R programming 7. The role of simulation in modern statistics 8. Regression models 9. Survival analysis and censored data 10. Structural equation models, factor analysis, and mediation 11. Predictive modelling and machine learning 12. Advanced topics 13. Debugging 14. Mathematical appendix Bibliography Index
| Erscheinungsdatum | 19.07.2024 |
|---|---|
| Zusatzinfo | 21 Line drawings, color; 20 Line drawings, black and white; 3 Halftones, color; 24 Illustrations, color; 20 Illustrations, black and white |
| Sprache | englisch |
| Maße | 178 x 254 mm |
| Gewicht | 907 g |
| Themenwelt | Mathematik / Informatik ► Mathematik ► Computerprogramme / Computeralgebra |
| Mathematik / Informatik ► Mathematik ► Statistik | |
| ISBN-10 | 1-032-51244-X / 103251244X |
| ISBN-13 | 978-1-032-51244-0 / 9781032512440 |
| Zustand | Neuware |
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