Learning path
Work through the starter material first, then move into core econometric concepts and data interpretation.
Module
A practical bridge into econometrics with beginner-friendly notation, summary statistics, uncertainty, and Python data workflows.
Module 1
Core ideas for thinking like an applied econometrician: models, variables, error terms, data structures, and interpretation.
Module 2
Estimate and interpret one-variable regression models with fitted lines, residuals, R-squared, Python output, and practice projects.
Module 3
Learn how multiple regression estimates partial relationships, controls for observed differences, diagnoses omitted-variable bias and collinearity, and supports responsible Python analysis.
Module 4
Learn how to make statistical inferences in multiple regression using t tests, p-values, confidence intervals, linear restrictions, F tests, and professional regression reporting.
Module 5
Learn how OLS behaves as sample size grows, why consistency matters, how large-sample inference works without normal errors, and how to use simulations and Python to understand asymptotic normality, asymptotic standard errors, LM tests, and asymptotic efficiency.
Module 6
Extend multiple regression beyond the linear-in-levels baseline by learning how scaling, logs, quadratics, interactions, adjusted R-squared, prediction, and careful controls change interpretation.
Module 7
Learn how to use qualitative information in regression with binary variables, categorical indicators, interactions, group-specific slopes, treatment evaluation, and linear probability models.
Module 8
Learn how changing error variance affects regression inference, how to use heteroskedasticity-robust standard errors and tests, how to diagnose heteroskedasticity, and when weighted least squares or feasible GLS may improve estimation.