eBook - ePub
Modern Statistics with R
From Wrangling and Exploring Data to Inference and Predictive Modelling
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- 492 pages
- English
- ePUB (mobile friendly)
- Only available on web
eBook - ePub
Modern Statistics with R
From Wrangling and Exploring Data to Inference and Predictive Modelling
Book details
Table of contents
Citations
About This Book
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.
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Yes, you can access Modern Statistics with R by Måns Thulin in PDF and/or ePUB format, as well as other popular books in Mathematics & Probability & Statistics. We have over one million books available in our catalogue for you to explore.
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Table of contents
- Cover Page
- Half-Title Page
- Title Page
- Copyright Page
- Dedication Page
- Contents
- List of Figures
- 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
- References
- Index