Statistics

Instructions

1. Write the model before deriving an estimator or test.
2. Keep p-values and posterior probabilities distinct.

Ordered items

1. Bias, variance, MSE, and consistencyStatistics · Easy · 7 min · Original
2. Maximum likelihood estimation from a densityStatistics · Medium · 12 min · External
3. Bayesian posterior density and normalizationStatistics · Hard · 15 min · External
4. Method of moments and Bayesian updatingStatistics · Medium · 10 min · Original
5. One-sided test, critical value, and p-valueStatistics · Medium · 12 min · External
6. Confidence intervals with known and unknown varianceStatistics · Easy · 14 min · External
7. Chi-square test for a contingency tableStatistics · Hard · 18 min · External
8. Simple linear regression from Gaussian maximum likelihoodStatistics · Medium · 25 min · External
9. Linear transformations and quadratic expectationsStatistics · Medium · 15 min · External
10. Type I error, Type II error, and powerStatistics · Medium · 5 min · External

Completion

Derive and interpret one estimator, interval, hypothesis test, and posterior update from first principles.