Statistical Inference
A 23-module path through statistical inference, in study order from foundations to the frontier.
Modules
- Populations, Samples, and Statistics
- Sampling Distributions
- The Sample Mean and Sample Variance
- Point Estimation and Estimators
- The Method of Moments
- Bias of an Estimator
- Variance and Mean Squared Error
- Maximum Likelihood Estimation
- Properties of MLEs
- Fisher Information and the Score
- The Cramer-Rao Lower Bound
- Sufficiency and the Factorization Theorem
- The Rao-Blackwell Theorem
- Confidence Intervals: Concept and Construction
- The t, Chi-Squared, and F Distributions
- Hypothesis Testing Framework
- The Neyman-Pearson Lemma
- p-values and Significance
- Standard Tests: t, Chi-Squared, and F
- Likelihood Ratio Tests
- Linear Regression and Least Squares
- The Gauss-Markov Theorem
- Bayesian Inference: An Introduction