Introduction to Econometrics: Regression Modeling Fundamentals

Econometrics provides the essential tools to test economic theories and analyze real-world data quantitatively. Start your journey into statistical modeling with this foundational course. By the end of this program, you will have a solid working knowledge of the Classical Linear Regression Model, capable of estimating simple and multiple regression models, performing hypothesis tests, and diagnosing common specification errors. What you'll learn: * Understand the fundamental assumptions and components of the Classical Linear Regression Model (CLRM). * Practice building and interpreting Simple and Multiple Linear Regression models for prediction and causal inference. * Apply statistical tests to determine significance, construct confidence intervals, and evaluate overall model fit. * Analyze and identify major violations of CLRM assumptions, including multicollinearity, heteroskedasticity, and autocorrelation. * Master the interpretation of regression output and model diagnostics to ensure reliable and reproducible analytical results. The course begins with core definitions and the mathematics of linear estimation before moving into practical model building. You will then explore essential model diagnostics and advanced topics like simultaneous equation systems. This course is designed for absolute beginners in econometrics, economics, or data analysis. No prior advanced statistical knowledge is required, just basic familiarity with algebra and statistics concepts. Start mastering the quantitative language of economics today.