Simulation-Based Inference for Regression

Traditional statistical methods often rely on strict assumptions about your data that do not hold true in real-world scenarios. This course introduces you to simulation-based inference, a modern, computational approach to analyzing regression models that uses computer-intensive resampling instead of rigid formulas. You will learn how to build and interpret confidence intervals for regression slopes using intuitive, data-driven simulations. What you will learn: Understand the foundational concepts of simulation-based inference and bootstrapping; Estimate the variability of a regression slope using resampling techniques; Construct and interpret confidence intervals for a slope; Conduct hypothesis testing for regression parameters using randomization; Apply modern diagnostics to assess the quality of your simulation results. We begin with core definitions and the fundamental logic of statistical inference before moving step-by-step through practical simulation workflows and analysis. This text-based course is designed for beginners in statistics and data analysis, with no advanced mathematical prerequisites required. Start learning how to analyze your data with modern, simulation-driven confidence.