Theory
Short explanations connect assumptions, equations, and economic meaning.
Learn Econometrics Interactively with Python
Ceteris Lab is a student-friendly learning space for building econometrics intuition with simple explanations, formulas, small datasets, Python examples, graphs, and quick quizzes.
Regression lab preview
OLS fitwageᵢ = β₀ + β₁ educᵢ + uᵢ
6modules
38+notebooks
Dataexamples
Why Ceteris Lab?
Built for learning by doing
Python practice
Run editable notebooks with pandas, matplotlib, and regression workflows.
Real data
Work with teaching datasets from wages, grades, firms, policy examples, and official indicators.
Interpretation
Translate coefficients, p-values, and model fit into careful language.
Module previews
Start with the foundations
Module
Math, Statistics, and Python Starter
A practical bridge into econometrics with beginner-friendly notation, summary statistics, uncertainty, and Python data workflows.
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4 to 6 hoursFoundational20 lessons0 notebooksLive
Module 1
Foundations of Econometrics
1
Core ideas for thinking like an applied econometrician: models, variables, error terms, data structures, and interpretation.
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3 to 4 hoursFoundational12 lessons0 notebooksLive
Module 2
Simple Regression
2
Estimate and interpret one-variable regression models with fitted lines, residuals, R-squared, Python output, and practice projects.
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4 to 6 hoursBeginner12 lessons0 notebooksLive
Module 3
Multiple Regression Analysis
3
Learn how multiple regression estimates partial relationships, controls for observed differences, diagnoses omitted-variable bias and collinearity, and supports responsible Python analysis.
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8 to 12 hoursIntermediate20 lessons0 notebooksLive
Module 4
Multiple Regression Inference
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.
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8 to 12 hoursIntermediate20 lessons0 notebooksLive
Module 5
OLS Asymptotics and Large-Sample Inference
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.
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6 to 9 hoursAdvanced Intermediate16 lessons0 notebooksLive
Module 6
Multiple Regression: Further Issues
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.
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8 to 12 hoursIntermediate20 lessons0 notebooksLive
Module 7
Qualitative Information and Binary Variables
7
Learn how to use qualitative information in regression with binary variables, categorical indicators, interactions, group-specific slopes, treatment evaluation, and linear probability models.
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9 to 13 hoursIntermediate20 lessons0 notebooksLive
Module 8
Heteroskedasticity
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.
Progress tracking available after sign-in
9 to 13 hoursIntermediate to Advanced Intermediate20 lessons0 notebooksLive