Polynomial Regression, R, and ggplot

A Practical Next Step in R Regression

This course builds directly on an earlier Simple Linear Regression course, moving learners from straight-line fits into polynomial regression using R and ggplot. Rather than treating polynomials as an abstract topic, the course walks through fitting curves to real data sets and visualizing the results, giving statistics learners a concrete, code-first way to see how model complexity affects fit.

A standout feature is the emphasis on writing custom R functions and graphing them with ggplot, a skill that carries over into almost any future data analysis project. The course closes with smoothing splines, a natural extension once polynomial fitting is understood.

Who it's for

Best suited to learners who already have R, RStudio, and a working knowledge of ggplot and basic regression, ideally from the instructor's prior course. At a relaxed pace, it's designed to fit into about a week.

Key takeaways

• Fitting polynomial models to data in R
• Writing and graphing custom R functions
• Using ggplot to visualize model fits
• An introduction to smoothing splines