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  1. Lecture 11. D) Testing Errors‏‎ (used on 1 page)
  2. Lecture 13. C) Karlin-Rubin Theorem‏‎ (used on 1 page)
  3. Lecture 15. E) Multiple Parameters‏‎ (used on 1 page)
  4. Lecture 2. A) Random Variables (cont.)‏‎ (used on 1 page)
  5. Lecture 5. C) Multiple Random Variables‏‎ (used on 1 page)
  6. Lecture 9. B) Evaluating Estimators‏‎ (used on 1 page)
  7. Lecture 11. E) Power Function‏‎ (used on 1 page)
  8. Lecture 13. D) 2-sided Tests and Unbiased Tests‏‎ (used on 1 page)
  9. Lecture 16. A) Bayesian Inference‏‎ (used on 1 page)
  10. Lecture 2. B) Leibniz Rule‏‎ (used on 1 page)
  11. Lecture 6. A) Multiple Random Variables (cont.)‏‎ (used on 1 page)
  12. Lecture 9. C) Minimum Variance Estimators‏‎ (used on 1 page)
  13. Lecture 11. F) Example 1‏‎ (used on 1 page)
  14. Lecture 13. E) p-value‏‎ (used on 1 page)
  15. Lecture 16. B) Example: Coin Tossing‏‎ (used on 1 page)
  16. Lecture 2. C) Transformations of Random Variables‏‎ (used on 1 page)
  17. Lecture 6. B) Conditional PMF/PDF‏‎ (used on 1 page)
  18. Lecture 9. D) Sufficient Statistics‏‎ (used on 1 page)
  19. Lecture 11. G) Setting the Critical Value‏‎ (used on 1 page)
  20. Lecture 13. F) Some Notes‏‎ (used on 1 page)
  21. Lecture 16. C) A More General Example‏‎ (used on 1 page)
  22. Lecture 3. A) Expected Value‏‎ (used on 1 page)
  23. Lecture 6. C) Conditional Moments‏‎ (used on 1 page)
  24. Lecture 9. E) Rao-Blackwell‏‎ (used on 1 page)
  25. Lecture 1. A) Sample Space‏‎ (used on 1 page)
  26. Lecture 12. A) Statistical Tests‏‎ (used on 1 page)
  27. Lecture 13. G) Interval Estimation/Confidence Intervals‏‎ (used on 1 page)
  28. Lecture 16. D) Conjugate Priors‏‎ (used on 1 page)
  29. Lecture 3. B) Moments‏‎ (used on 1 page)
  30. Lecture 6. D) Law of Iterated Expectations‏‎ (used on 1 page)
  31. Lecture 9. F) Factorization Theorem‏‎ (used on 1 page)
  32. Lecture 1. B) Probability Function‏‎ (used on 1 page)
  33. Lecture 12. B) Likelihood-Ratio Test‏‎ (used on 1 page)
  34. Lecture 14. A) Convergence‏‎ (used on 1 page)
  35. Lecture 16. E) Normal Distribution‏‎ (used on 1 page)
  36. Lecture 3. C) Moment Generating Function‏‎ (used on 1 page)
  37. Lecture 6. E) Conditional Variance Identity‏‎ (used on 1 page)
  38. Lecture 1. C) Domain of Probability Function‏‎ (used on 1 page)
  39. Lecture 12. C) Lagrange Multiplier Test‏‎ (used on 1 page)
  40. Lecture 14. B) Law of Large Numbers‏‎ (used on 1 page)
  41. Lecture 16. F) "Counterexample"‏‎ (used on 1 page)
  42. Lecture 4. A) Distributions‏‎ (used on 1 page)
  43. Lecture 6. F) Covariance and Correlation‏‎ (used on 1 page)
  44. Lecture 1. D) Probability Space‏‎ (used on 1 page)
  45. Lecture 12. D) Wald Test‏‎ (used on 1 page)
  46. Lecture 14. C) Convergence in Distribution‏‎ (used on 1 page)
  47. Lecture 16. G) Multiple Observations‏‎ (used on 1 page)
  48. Lecture 4. B) Bernoulli‏‎ (used on 1 page)
  49. Lecture 6. G) Some Inequalities‏‎ (used on 1 page)
  50. Lecture 1. E) More on Probability Functions‏‎ (used on 1 page)
  51. Lecture 12. E) Example: LRT‏‎ (used on 1 page)
  52. Lecture 14. D) Slutsky’s Theorem‏‎ (used on 1 page)
  53. Lecture 16. H) Theorem: Berstein von-Mises‏‎ (used on 1 page)
  54. Lecture 4. C) Binomial‏‎ (used on 1 page)
  55. Lecture 7. A) Random Sample‏‎ (used on 1 page)
  56. Lecture 1. F) Random Variables‏‎ (used on 1 page)
  57. Lecture 12. F) Test Equivalence‏‎ (used on 1 page)
  58. Lecture 14. E) Central Limit Theorem‏‎ (used on 1 page)
  59. Lecture 17. A) Ordinary Least Squares‏‎ (used on 1 page)
  60. Lecture 4. D) Poisson‏‎ (used on 1 page)
  61. Lecture 7. B) Statistics‏‎ (used on 1 page)
  62. Lecture 10. A) Finding UMVU Estimators‏‎ (used on 1 page)
  63. Lecture 12. G) Equivalence Between LRT and LM Tests‏‎ (used on 1 page)
  64. Lecture 14. F) Delta Method‏‎ (used on 1 page)
  65. Lecture 17. B) Normal Linear Model‏‎ (used on 1 page)
  66. Lecture 4. E) Uniform‏‎ (used on 1 page)
  67. Lecture 7. C) Order Statistics‏‎ (used on 1 page)
  68. Lecture 10. B) Complete Statistic‏‎ (used on 1 page)
  69. Lecture 12. H) Equivalence Between LRT and Wald Tests‏‎ (used on 1 page)
  70. Lecture 14. G) Somewhat Pedantic Remark on Notation‏‎ (used on 1 page)
  71. Lecture 17. C) Asymptotic Properties of OLS‏‎ (used on 1 page)
  72. Lecture 4. F) Gamma‏‎ (used on 1 page)
  73. Lecture 7. D) Statistical Inference‏‎ (used on 1 page)
  74. Lecture 10. C) Cramer-Rao Lower Bound‏‎ (used on 1 page)
  75. Lecture 12. I) Optimal Tests‏‎ (used on 1 page)
  76. Lecture 15. A) Asymptotic Properties of ML Estimators‏‎ (used on 1 page)
  77. Lecture 17. D) Bootstrapping‏‎ (used on 1 page)
  78. Lecture 4. G) Normal‏‎ (used on 1 page)
  79. Lecture 8. A) Point Estimation‏‎ (used on 1 page)
  80. Lecture 11. A) Hypothesis Testing‏‎ (used on 1 page)
  81. Lecture 12. J) Neyman-Pearson Lemma‏‎ (used on 1 page)
  82. Lecture 15. B) Some Implications‏‎ (used on 1 page)
  83. Lecture 18. A) Multicollinearity‏‎ (used on 1 page)
  84. Lecture 4. H) Dirac delta function‏‎ (used on 1 page)
  85. Lecture 8. B) Method of Moments‏‎ (used on 1 page)
  86. Lecture 11. B) Testing Procedure‏‎ (used on 1 page)
  87. Lecture 13. A) Test Optimality (cont.)‏‎ (used on 1 page)
  88. Lecture 15. C) Example: Hypothesis Test‏‎ (used on 1 page)
  89. Lecture 18. B) Partitioned Regression‏‎ (used on 1 page)
  90. Lecture 5. A) Families of Distributions‏‎ (used on 1 page)
  91. Lecture 8. C) Maximum Likelihood‏‎ (used on 1 page)
  92. Lecture 11. C) Variation on a Theme‏‎ (used on 1 page)
  93. Lecture 13. B) Example: Normal‏‎ (used on 1 page)
  94. Lecture 15. D) Example: Exponential Distribution‏‎ (used on 1 page)
  95. Lecture 18. C) Gauss-Markov Theorem‏‎ (used on 1 page)
  96. Lecture 5. B) Chebychev's Inequality‏‎ (used on 1 page)
  97. Lecture 9. A) Point Estimation (cont.)‏‎ (used on 1 page)

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