Applied Linear Regression Modeling in R

Statistical modeling is an essential skill for uncovering trends and making accurate, data-driven predictions. This text-based course guides you through the principles and practical mechanics of linear regression in R, providing a direct path from theoretical foundations to applied statistical analysis. You will begin by mastering core statistical concepts and regression terminology before moving on to constructing models, interpreting diagnostic statistics, and refining your results across varied datasets. What you will learn: - Understand foundational statistical terms, regression mechanics, and formula syntax - Fit simple and multiple linear regression models in R - Evaluate model performance and accuracy using R-squared, residuals, and error metrics - Tidy and extract model outputs efficiently using modern tools such as broom - Incorporate categorical variables, interaction effects, and basic non-linear transformations - Apply diagnostic checks to identify model assumptions and detect outliers - Practice building end-to-end predictive workflows on structured data The course begins with foundational definitions and data preparation concepts before moving sequentially into model fitting, interpretation, and multi-model analysis. Designed for beginners and aspiring data analysts, this course requires no advanced statistical background. Start reading today to build reliable linear models with confidence in R.