Linear Regression in R: Build and Optimize Predictive Models

Linear regression is the cornerstone of predictive modeling and data analysis. Understanding how to build, interpret, and refine these models in R is an essential skill for anyone looking to work with data. In this written course, you will transition from a beginner to a confident practitioner capable of implementing regression analysis. You will start with core statistical foundations before moving on to practical coding, model diagnostics, and optimization techniques using modern R libraries. What you'll learn: - Understand the foundational theory, terminology, and assumptions of linear regression. - Build simple and multiple linear regression models using R's standard and modern modeling syntax. - Evaluate model performance using key metrics such as R-squared, residual analysis, and error rates. - Optimize regression models through feature selection, handling multicollinearity, and transforming variables. - Apply modern R workflows using tidyverse principles to clean data and prepare it for regression analysis. - Interpret model coefficients to make valid statistical inferences and business recommendations. This structured text-based course guides you step-by-step from foundational statistical concepts to hands-on model building and optimization. You will read clear explanations, analyze code snippets, and complete written exercises to solidify your understanding. This course is designed for beginners in data science, analytics, or research who want to learn regression modeling. No prior experience with statistics or R programming is required, as we start from the absolute basics. Start reading today to master the fundamentals of predictive modeling with R.