1 · Probability models and random variables
Build coherent probability models and translate them into simulated data.
Statistical Foundations, Simulation & Asymptotics
Probability, sampling distributions, estimator properties, asymptotics, Monte Carlo, and bootstrap inference.
Course overview
Learn Statistical Foundations, Simulation & Asymptotics interactively
This free course uses real Python and statsmodels in your browser. Work through the modules below with explanations, executable examples, interactive controls, and questions.
2 · Sampling distributions
See estimators as random variables across repeated samples.
3 · Estimator properties
Separate bias, variance, consistency, and efficiency.
4 · LLN, CLT, and asymptotic inference
Connect probability limits to usable large-sample uncertainty.
5 · Monte Carlo and bootstrap
Validate estimators through controlled repetition and defensible resampling.