The past decades have transformed the world of statistical data analysis, with new methods, new types of data, and new computational tools. Modern Statistics with R introduces you to key parts of this modern statistical toolkit. It teaches you:
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.
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
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