Simple Linear Regression Theory and Fundamentals

Understanding how variables relate to one another is the cornerstone of statistical analysis and predictive modeling. This course provides a clear, text-based exploration of simple linear regression theory, breaking down core mathematical concepts into intuitive, approachable explanations. You will gain a solid conceptual grounding in how linear models work under the hood without getting lost in unnecessary jargon. What you'll learn: Understand core statistical definitions, variables, and the regression equation; Calculate slope, intercept, and the line of best fit using ordinary least squares; Evaluate model accuracy through residuals, variance, and the coefficient of determination (R-squared); Assess fundamental regression assumptions including linearity, homoscedasticity, and independence; Interpret regression outputs confidently to make sound data-driven inferences; Connect linear regression concepts to modern predictive workflows in data analysis. The course starts with essential terminology and statistical definitions before stepping through theoretical derivations, performance metrics, and model evaluation techniques. This guide is designed for beginners, students, and aspiring data analysts who want to build genuine conceptual clarity with no prior advanced mathematics required. Start reading today to build a strong theoretical foundation in linear regression.