Linear Regression in R for Beginners

Understanding how variables relate to one another is the cornerstone of data analysis and predictive modeling. This text-based course guides you from the absolute basics of statistical relationships to constructing your first predictive models using R. You will learn to clean your data, fit regression lines, and interpret the output with confidence. By completing this course, you will transform raw data into actionable insights, understanding not just the code to run but the statistical theory behind your results. What you'll learn: - Understand the core mathematical concepts of simple and multiple linear regression - Import, clean, and prepare datasets for regression analysis inside R - Fit linear models using standard R formulas and interpret coefficients accurately - Evaluate model fit using R-squared, residual analysis, and diagnostic plots - Apply modern tidyverse workflows for cleaner, more maintainable data science code - Make predictions on new data and calculate confidence intervals for your estimates We begin with foundational statistical definitions and simple correlation before moving step-by-step through model building, diagnostic checking, and multiple regression. The course is designed entirely for beginners with no prior modeling experience, though a basic familiarity with R syntax is helpful. Start reading today to master the fundamentals of predictive modeling in R.