Linear Regression with One Numerical Explanatory Variable

Understanding how one variable influences another is a cornerstone of data analysis and predictive modeling. This course guides you through the fundamental principles of simple linear regression, showing you how to model and interpret the relationship between a single numerical explanatory variable and a response variable. You will transition from grasping basic correlation to confidently constructing and evaluating your own predictive models. What you will learn: Understand the core concepts of independent and dependent variables in statistical modeling; Calculate and interpret correlation coefficients to measure relationship strength; Fit a simple linear regression line and interpret its slope and intercept; Evaluate model fit using residuals and the coefficient of determination; Apply modern diagnostic techniques to check model assumptions and identify outliers. The course begins with essential terminology and the mathematical foundations of scatterplots and correlation, before guiding you step-by-step through regression calculations, model diagnostics, and practical interpretation. This course is designed for beginners, data enthusiasts, and aspiring analysts who want to build a solid foundation in regression analysis without needing prior advanced statistical training. Start reading today to unlock the predictive power of your numerical data.