Applied Statistical Inference in R

This unit builds on prior R fundamentals to combine data wrangling, visualization, programming constructs, and inferential statistics into end-to-end analyses and projects. Students will practice using the tidyverse and base R to summarize and visualize data, write reusable functions and control flow, import/export datasets, and perform and interpret t-tests, variance tests, and chi-square analyses.

Learning Objectives

• analyze datasets using tidyverse and base R functions to summarize central tendency (median, mode), variability, and overall structure of data frames
• apply inferential procedures in R—one-sample and two-sample t-tests, variance tests, and chi-square tests—interpreting outputs and visualizing relevant t and chi-square distributions
• demonstrate advanced R programming techniques by creating and debugging user-defined functions, using for loops and if statements, and annotating code with comments for reproducibility
• evaluate end-to-end data workflows by importing and exporting data, iteratively refining interactive graphics, and completing reproducible project analyses