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101. Lecture 14. A) Convergence‏‎ (23:04, 13 November 2019)
102. Lecture 14. B) Law of Large Numbers‏‎ (23:06, 13 November 2019)
103. Lecture 14. E) Central Limit Theorem‏‎ (19:19, 14 November 2019)
104. Lecture 15. E) Multiple Parameters‏‎ (17:47, 19 November 2019)
105. Lecture 16. B) Example: Coin Tossing‏‎ (18:26, 20 November 2019)
106. Lecture 16. D) Conjugate Priors‏‎ (18:31, 20 November 2019)
107. Lecture 16. E) Normal Distribution‏‎ (18:35, 20 November 2019)
108. Lecture 16. F) "Counterexample"‏‎ (18:36, 20 November 2019)
109. Lecture 16. H) Theorem: Berstein von-Mises‏‎ (18:41, 20 November 2019)
110. Lecture 15. C) Example: Hypothesis Test‏‎ (20:04, 21 November 2019)
111. Lecture 17. A) Ordinary Least Squares‏‎ (23:18, 2 December 2019)
112. Lecture 17. D) Bootstrapping‏‎ (23:46, 2 December 2019)
113. Lecture 17. B) Normal Linear Model‏‎ (00:19, 4 December 2019)
114. Lecture 17. C) Asymptotic Properties of OLS‏‎ (00:20, 4 December 2019)
115. Lecture 15. A) Asymptotic Properties of ML Estimators‏‎ (00:22, 4 December 2019)
116. Lecture 18. A) Multicollinearity‏‎ (19:57, 4 December 2019)
117. Lecture 18. B) Partitioned Regression‏‎ (20:02, 4 December 2019)
118. Lecture 18. C) Gauss-Markov Theorem‏‎ (00:18, 5 December 2019)

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