# Learn R for Applied Statistics

## With Data Visualizations, Regressions, and Statistics

• Eric Goh Ming Hui 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

### 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