Linear Regression for Data Science with R

Linear regression remains one of the most fundamental and widely used statistical tools in data science. Whether you want to predict housing prices, analyze business trends, or understand relationships between variables, mastering this technique is an essential first step. This written course guides you through the theory and practical application of linear regression using R. You will start with the core statistical concepts explained in plain language, then move into writing clean, efficient code to build and evaluate your own predictive models. What you'll learn: - Understand the foundational concepts and terminology behind linear regression. - Prepare and explore datasets using modern R conventions, including the tidyverse. - Build simple and multiple linear regression models through guided text-based coding exercises. - Evaluate model accuracy and interpret statistical coefficients to make data-driven decisions. - Apply modern modeling workflows to structure, test, and refine your predictions effectively. The curriculum flows logically from basic statistical definitions to hands-on coding practice. You will read clear explanations, study practical code snippets, and work through written exercises that reinforce your new skills without needing to navigate complex setups. This course is designed for absolute beginners to data science. No prior statistical knowledge is required, making it the perfect starting point for your analytical journey. Start reading today to unlock the power of predictive modeling with R.