Statistics 110 - Probability

Statistics 110 - Probability

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Lecture 8: Random Variables and Their Distributions | Statistics 110

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8 of 35

Lecture 8: Random Variables and Their Distributions | Statistics 110

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Statistics 110 - Probability

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  1. 1 Lecture 1: Probability and Counting | Statistics 110
  2. 2 Lecture 2: Story Proofs, Axioms of Probability | Statistics 110
  3. 3 Lecture 3: Birthday Problem, Properties of Probability | Statistics 110
  4. 4 Lecture 4: Conditional Probability | Statistics 110
  5. 5 Lecture 5: Conditioning Continued, Law of Total Probability | Statistics 110
  6. 6 Lecture 6: Monty Hall, Simpson's Paradox | Statistics 110
  7. 7 Lecture 7: Gambler's Ruin and Random Variables | Statistics 110
  8. 8 Lecture 8: Random Variables and Their Distributions | Statistics 110
  9. 9 Lecture 9: Expectation, Indicator Random Variables, Linearity | Statistics 110
  10. 10 Lecture 10: Expectation Continued | Statistics 110
  11. 11 Lecture 11: The Poisson distribution | Statistics 110
  12. 12 Lecture 12: Discrete vs. Continuous, the Uniform | Statistics 110
  13. 13 Lecture 13: Normal distribution | Statistics 110
  14. 14 Lecture 14: Location, Scale, and LOTUS | Statistics 110
  15. 15 Lecture 15: Midterm Review | Statistics 110
  16. 16 Lecture 16: Exponential Distribution | Statistics 110
  17. 17 Lecture 17: Moment Generating Functions | Statistics 110
  18. 18 Lecture 18: MGFs Continued | Statistics 110
  19. 19 Lecture 19: Joint, Conditional, and Marginal Distributions | Statistics 110
  20. 20 Lecture 20: Multinomial and Cauchy | Statistics 110
  21. 21 Lecture 21: Covariance and Correlation | Statistics 110
  22. 22 Lecture 22: Transformations and Convolutions | Statistics 110
  23. 23 Lecture 23: Beta distribution | Statistics 110
  24. 24 Lecture 24: Gamma distribution and Poisson process | Statistics 110
  25. 25 Lecture 25: Order Statistics and Conditional Expectation | Statistics 110
  26. 26 Lecture 26: Conditional Expectation Continued | Statistics 110
  27. 27 Lecture 27: Conditional Expectation given an R.V. | Statistics 110
  28. 28 Lecture 28: Inequalities | Statistics 110
  29. 29 Lecture 29: Law of Large Numbers and Central Limit Theorem | Statistics 110
  30. 30 Lecture 30: Chi-Square, Student-t, Multivariate Normal | Statistics 110
  31. 31 Lecture 31: Markov Chains | Statistics 110
  32. 32 Lecture 32: Markov Chains Continued | Statistics 110
  33. 33 Lecture 33: Markov Chains Continued Further | Statistics 110
  34. 34 Lecture 34: A Look Ahead | Statistics 110
  35. 35 Joseph Blitzstein: "The Soul of Statistics" | Harvard Thinks Big 4

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