Modern Statistics with R: From Wrangling and Exploring Data to Inference and Predictive Modelling
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.

1139979246
Modern Statistics with R: From Wrangling and Exploring Data to Inference and Predictive Modelling
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.

84.99 In Stock
Modern Statistics with R: From Wrangling and Exploring Data to Inference and Predictive Modelling

Modern Statistics with R: From Wrangling and Exploring Data to Inference and Predictive Modelling

by Måns Thulin
Modern Statistics with R: From Wrangling and Exploring Data to Inference and Predictive Modelling
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Modern Statistics with R: From Wrangling and Exploring Data to Inference and Predictive Modelling

Modern Statistics with R: From Wrangling and Exploring Data to Inference and Predictive Modelling

by Måns Thulin

Overview

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.

Product Details

ISBN-13: 9781032512440
Publisher: CRC Press
Publication date: 08/20/2024
Edition description: 2nd ed.
Pages: 492
Product dimensions: 7.00(w) x 10.00(h) x (d)

About the Author

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.

Table of Contents

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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