Learn R for Applied Statistics: With Data Visualizations, Regressions, and Statistics

Description

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. What You Will Learn * 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 Who This Book Is For Those who are interested in data science, in particular data exploration using applied statistics, and the use of R programming for data visualizations. Front Matter ....Pages i-xv Introduction (Eric Goh Ming Hui)....Pages 1-18 Getting Started (Eric Goh Ming Hui)....Pages 19-37 Basic Syntax (Eric Goh Ming Hui)....Pages 39-86 Descriptive Statistics (Eric Goh Ming Hui)....Pages 87-127 Data Visualizations (Eric Goh Ming Hui)....Pages 129-172 Inferential Statistics and Regressions (Eric Goh Ming Hui)....Pages 173-236 Back Matter ....Pages 237-243