Inferential Statistics and R Programming

This unit advances R skills for applied data science by teaching tidyverse data manipulation, user-defined functions, control structures, and reproducible project workflows. Students learn to compute and visualize summary statistics, perform inferential tests (t-tests, variance tests, chi-square), and document and export analyses to prepare for more advanced modeling and applied projects.

Learning Objectives

• Analyze datasets using tidyverse tools to compute and interpret summary statistics (mean, median, mode) and create informative visualizations of distributions
• Apply R programming constructs—built-in and user-defined functions, data structures, for loops, if statements, and commenting—to develop reproducible, well-documented analysis scripts
• Demonstrate the ability to perform and interpret inferential statistics in R, including one-sample and two-sample t-tests, tests of variance, and chi-square tests, and visualize associated t and chi-square distributions
• Construct end-to-end analysis projects by importing/exporting data, running appropriate statistical tests, summarizing results, and communicating findings through annotated code and graphics