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- Lecture 11. D) Testing Errors (used on 1 page)
- Lecture 13. C) Karlin-Rubin Theorem (used on 1 page)
- Lecture 15. E) Multiple Parameters (used on 1 page)
- Lecture 2. A) Random Variables (cont.) (used on 1 page)
- Lecture 5. C) Multiple Random Variables (used on 1 page)
- Lecture 9. B) Evaluating Estimators (used on 1 page)
- Lecture 11. E) Power Function (used on 1 page)
- Lecture 13. D) 2-sided Tests and Unbiased Tests (used on 1 page)
- Lecture 16. A) Bayesian Inference (used on 1 page)
- Lecture 2. B) Leibniz Rule (used on 1 page)
- Lecture 6. A) Multiple Random Variables (cont.) (used on 1 page)
- Lecture 9. C) Minimum Variance Estimators (used on 1 page)
- Lecture 11. F) Example 1 (used on 1 page)
- Lecture 13. E) p-value (used on 1 page)
- Lecture 16. B) Example: Coin Tossing (used on 1 page)
- Lecture 2. C) Transformations of Random Variables (used on 1 page)
- Lecture 6. B) Conditional PMF/PDF (used on 1 page)
- Lecture 9. D) Sufficient Statistics (used on 1 page)
- Lecture 11. G) Setting the Critical Value (used on 1 page)
- Lecture 13. F) Some Notes (used on 1 page)
- Lecture 16. C) A More General Example (used on 1 page)
- Lecture 3. A) Expected Value (used on 1 page)
- Lecture 6. C) Conditional Moments (used on 1 page)
- Lecture 9. E) Rao-Blackwell (used on 1 page)
- Lecture 1. A) Sample Space (used on 1 page)
- Lecture 12. A) Statistical Tests (used on 1 page)
- Lecture 13. G) Interval Estimation/Confidence Intervals (used on 1 page)
- Lecture 16. D) Conjugate Priors (used on 1 page)
- Lecture 3. B) Moments (used on 1 page)
- Lecture 6. D) Law of Iterated Expectations (used on 1 page)
- Lecture 9. F) Factorization Theorem (used on 1 page)
- Lecture 1. B) Probability Function (used on 1 page)
- Lecture 12. B) Likelihood-Ratio Test (used on 1 page)
- Lecture 14. A) Convergence (used on 1 page)
- Lecture 16. E) Normal Distribution (used on 1 page)
- Lecture 3. C) Moment Generating Function (used on 1 page)
- Lecture 6. E) Conditional Variance Identity (used on 1 page)
- Lecture 1. C) Domain of Probability Function (used on 1 page)
- Lecture 12. C) Lagrange Multiplier Test (used on 1 page)
- Lecture 14. B) Law of Large Numbers (used on 1 page)
- Lecture 16. F) "Counterexample" (used on 1 page)
- Lecture 4. A) Distributions (used on 1 page)
- Lecture 6. F) Covariance and Correlation (used on 1 page)
- Lecture 1. D) Probability Space (used on 1 page)
- Lecture 12. D) Wald Test (used on 1 page)
- Lecture 14. C) Convergence in Distribution (used on 1 page)
- Lecture 16. G) Multiple Observations (used on 1 page)
- Lecture 4. B) Bernoulli (used on 1 page)
- Lecture 6. G) Some Inequalities (used on 1 page)
- Lecture 1. E) More on Probability Functions (used on 1 page)
- Lecture 12. E) Example: LRT (used on 1 page)
- Lecture 14. D) Slutsky’s Theorem (used on 1 page)
- Lecture 16. H) Theorem: Berstein von-Mises (used on 1 page)
- Lecture 4. C) Binomial (used on 1 page)
- Lecture 7. A) Random Sample (used on 1 page)
- Lecture 1. F) Random Variables (used on 1 page)
- Lecture 12. F) Test Equivalence (used on 1 page)
- Lecture 14. E) Central Limit Theorem (used on 1 page)
- Lecture 17. A) Ordinary Least Squares (used on 1 page)
- Lecture 4. D) Poisson (used on 1 page)
- Lecture 7. B) Statistics (used on 1 page)
- Lecture 10. A) Finding UMVU Estimators (used on 1 page)
- Lecture 12. G) Equivalence Between LRT and LM Tests (used on 1 page)
- Lecture 14. F) Delta Method (used on 1 page)
- Lecture 17. B) Normal Linear Model (used on 1 page)
- Lecture 4. E) Uniform (used on 1 page)
- Lecture 7. C) Order Statistics (used on 1 page)
- Lecture 10. B) Complete Statistic (used on 1 page)
- Lecture 12. H) Equivalence Between LRT and Wald Tests (used on 1 page)
- Lecture 14. G) Somewhat Pedantic Remark on Notation (used on 1 page)
- Lecture 17. C) Asymptotic Properties of OLS (used on 1 page)
- Lecture 4. F) Gamma (used on 1 page)
- Lecture 7. D) Statistical Inference (used on 1 page)
- Lecture 10. C) Cramer-Rao Lower Bound (used on 1 page)
- Lecture 12. I) Optimal Tests (used on 1 page)
- Lecture 15. A) Asymptotic Properties of ML Estimators (used on 1 page)
- Lecture 17. D) Bootstrapping (used on 1 page)
- Lecture 4. G) Normal (used on 1 page)
- Lecture 8. A) Point Estimation (used on 1 page)
- Lecture 11. A) Hypothesis Testing (used on 1 page)
- Lecture 12. J) Neyman-Pearson Lemma (used on 1 page)
- Lecture 15. B) Some Implications (used on 1 page)
- Lecture 18. A) Multicollinearity (used on 1 page)
- Lecture 4. H) Dirac delta function (used on 1 page)
- Lecture 8. B) Method of Moments (used on 1 page)
- Lecture 11. B) Testing Procedure (used on 1 page)
- Lecture 13. A) Test Optimality (cont.) (used on 1 page)
- Lecture 15. C) Example: Hypothesis Test (used on 1 page)
- Lecture 18. B) Partitioned Regression (used on 1 page)
- Lecture 5. A) Families of Distributions (used on 1 page)
- Lecture 8. C) Maximum Likelihood (used on 1 page)
- Lecture 11. C) Variation on a Theme (used on 1 page)
- Lecture 13. B) Example: Normal (used on 1 page)
- Lecture 15. D) Example: Exponential Distribution (used on 1 page)
- Lecture 18. C) Gauss-Markov Theorem (used on 1 page)
- Lecture 5. B) Chebychev's Inequality (used on 1 page)
- Lecture 9. A) Point Estimation (cont.) (used on 1 page)