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Learn R for Applied Statistics

With Data Visualizations, Regressions, and Statistics

  • Eric Goh Ming Hui

Table of contents

  1. Front Matter
    Pages i-xv
  2. Eric Goh Ming Hui
    Pages 1-18
  3. Eric Goh Ming Hui
    Pages 19-37
  4. Eric Goh Ming Hui
    Pages 39-86
  5. Eric Goh Ming Hui
    Pages 87-127
  6. Eric Goh Ming Hui
    Pages 129-172
  7. Eric Goh Ming Hui
    Pages 173-236
  8. Back Matter
    Pages 237-243

About this book

Introduction

Gain the R programming language fundamentals for doing the applied statistics useful for data exploration and analysis in data science and data mining. This book covers topics ranging from R syntax basics, descriptive statistics, and data visualizations to inferential statistics and regressions. After learning R’s syntax, you will work through data visualizations such as histograms and boxplot charting, descriptive statistics, and inferential statistics such as t-test, chi-square test, ANOVA, non-parametric test, and linear regressions. 

Learn R for Applied Statistics is a timely skills-migration book that equips you with the R programming fundamentals and introduces you to applied statistics for data explorations. 

You will:
  • Discover R, statistics, data science, data mining, and big data
  • Master the fundamentals of R programming, including variables and arithmetic, vectors, lists, data frames, conditional statements, loops, and functions
  • Work with descriptive statistics 
  • Create data visualizations, including bar charts, line charts, scatter plots, boxplots, histograms, and scatterplots
  • Use inferential statistics including t-tests, chi-square tests, ANOVA, non-parametric tests, linear regressions, and multiple linear regressions

Keywords

Statistics R Data Science Data Mining Data Vizualisation Data Exploration Data Analytics Machine Learning Natural Language Processing

Authors and affiliations

  • Eric Goh Ming Hui
    • 1
  1. 1.SingaporeSingapore

Bibliographic information