Linear Regression in R for Data Science

Linear regression is the cornerstone of predictive modeling and data analysis, serving as the starting point for anyone entering the field of data science. Understanding how to build, interpret, and validate these models allows you to uncover relationships in your data and make accurate, data-driven predictions. This text-based course guides you from the fundamental mathematics of regression to implementing robust models in R. You will transition from understanding simple correlation to building, evaluating, and refining multi-variable models. Through structured written explanations, you will learn to interpret statistical outputs, diagnose model assumptions, and prepare your data for analysis using modern R packages. What you'll learn: - Understand the core mathematical concepts and assumptions behind linear regression - Implement simple and multiple linear regression models using R - Interpret model coefficients, residuals, R-squared values, and p-values - Diagnose and resolve common model issues like multicollinearity and heteroscedasticity - Prepare and clean data using modern tidyverse workflows before modeling - Evaluate model performance and make predictions on new data sets This course begins with essential statistical terminology and the foundational theory of ordinary least squares. You will then progress step-by-step through practical coding workflows in R, learning how to fit models, validate assumptions, and communicate your findings clearly. This course is designed for beginners, aspiring data scientists, and analysts who want to build a strong foundation in statistical modeling. No prior experience with regression or advanced mathematics is required, though a basic familiarity with R syntax is helpful. Start reading today to unlock the predictive power of linear regression in your data workflows.