Linear Regression with Categorical Variables in R

Data in the real world is rarely just numbers. To make accurate predictions and draw meaningful conclusions, you must understand how to incorporate categorical factors—like treatment groups, regions, or education levels—into your regression models. This text-based course guides you through the foundational concepts and practical steps of running linear regressions with categorical explanatory variables. You will start by mastering key statistical terms, understanding dummy coding, and learning how to interpret coefficients when working with non-numeric data. From there, you will transition into writing clean, modern R code to fit, evaluate, and visualize your models. What you'll learn: - Understand the foundational theory of linear regression with qualitative predictors - Master dummy coding and reference group selection in R - Interpret regression coefficients and intercept values for categorical data - Evaluate model fit using modern diagnostic tools and residuals analysis - Generate clear, readable regression summaries and interpret p-values - Apply your skills to real-world datasets using clean, reproducible R workflows This course begins with core definitions and statistical principles before moving step-by-step into hands-on R programming. You will read structured explanations, analyze clear code snippets, and complete written exercises designed to solidify your understanding. This course is designed for beginners, data analysts, and researchers who want to expand their statistical toolkit. No prior experience with regression modeling is required, though a basic familiarity with R is helpful. Start modeling your categorical data with confidence today.