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

by: Måns Thulin (Author)

Publisher: Chapman and Hall/CRC

Edition: 2nd

Publication Date: 2024/8/20

Language: English

Print Length: 474 pages

ISBN-10: 103251244X

ISBN-13: 9781032512440

Book Description

The past decades have transformed the world of statistical data analysis, with new methods, new types of data, and new computational tools. Mode Statistics with R introduces you to key parts of this mode 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 – mode methods for testing hypotheses and computing confidence intervals.Predictive modelling – regression models and machine leaing 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.modestatisticswithr.com.

About the Author

The past decades have transformed the world of statistical data analysis, with new methods, new types of data, and new computational tools. Mode Statistics with R introduces you to key parts of this mode 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 – mode methods for testing hypotheses and computing confidence intervals.Predictive modelling – regression models and machine leaing 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.modestatisticswithr.com.