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Showing below up to 50 results in range #51 to #100.
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- Full Lecture 7 (3 revisions)
- Lecture 12. F) Test Equivalence (3 revisions)
- Lecture 16. B) Example: Coin Tossing (3 revisions)
- Lecture 9. B) Evaluating Estimators (3 revisions)
- Lecture 7. A) Random Sample (3 revisions)
- Full Lecture 15 (3 revisions)
- Lecture 17. A) Ordinary Least Squares (3 revisions)
- Lecture 11. E) Power Function (4 revisions)
- Lecture 3. B) Moments (4 revisions)
- Lecture 16. E) Normal Distribution (4 revisions)
- Lecture 15. A) Asymptotic Properties of ML Estimators (4 revisions)
- Lecture 16. D) Conjugate Priors (4 revisions)
- Lecture 6. F) Covariance and Correlation (4 revisions)
- Lecture 9. F) Factorization Theorem (4 revisions)
- Lecture 7. C) Order Statistics (4 revisions)
- Lecture 7. B) Statistics (4 revisions)
- Lecture 11. G) Setting the Critical Value (4 revisions)
- Lecture 13. G) Interval Estimation/Confidence Intervals (4 revisions)
- Lecture 13. F) Some Notes (4 revisions)
- Lecture 8. C) Maximum Likelihood (4 revisions)
- Lecture 14. E) Central Limit Theorem (4 revisions)
- Lecture 5. C) Multiple Random Variables (4 revisions)
- Lecture 18. C) Gauss-Markov Theorem (4 revisions)
- Lecture 17. B) Normal Linear Model (4 revisions)
- Lecture 6. D) Law of Iterated Expectations (4 revisions)
- Lecture 12. H) Equivalence Between LRT and Wald Tests (4 revisions)
- Lecture 11. B) Testing Procedure (4 revisions)
- Lecture 3. A) Expected Value (4 revisions)
- Lecture 11. F) Example 1 (5 revisions)
- Lecture 14. A) Convergence (5 revisions)
- Lecture 12. D) Wald Test (5 revisions)
- Lecture 4. E) Uniform (5 revisions)
- Lecture 11. C) Variation on a Theme (5 revisions)
- Lecture 12. C) Lagrange Multiplier Test (5 revisions)
- Lecture 12. G) Equivalence Between LRT and LM Tests (5 revisions)
- Lecture 6. C) Conditional Moments (5 revisions)
- Lecture 4. C) Binomial (6 revisions)
- Lecture 12. J) Neyman-Pearson Lemma (6 revisions)
- Lecture 1. D) Probability Space (6 revisions)
- Lecture 15. C) Example: Hypothesis Test (6 revisions)
- Lecture 6. A) Multiple Random Variables (cont.) (6 revisions)
- Lecture 17. C) Asymptotic Properties of OLS (6 revisions)
- Lecture 13. B) Example: Normal (6 revisions)
- Lecture 2. B) Leibniz Rule II (6 revisions)
- Lecture 10. A) Finding UMVU Estimators (6 revisions)
- Lecture 12. E) Example: LRT (6 revisions)
- Lecture 15. E) Multiple Parameters (6 revisions)
- Lecture 13. E) p-value (6 revisions)
- Lecture 11. D) Testing Errors (6 revisions)
- Lecture 4. D) Poisson (7 revisions)