Statistics 110: Probability
4.0
(2)
21 learners
What you'll learn
This course includes
- 27 hours of video
- Certificate of completion
- Access on mobile and TV
Course content
1 modules • 35 lessons • 27 hours of video
Statistics 110: Probability
35 lessons
• 27 hours
Statistics 110: Probability
35 lessons
• 27 hours
- Lecture 1: Probability and Counting | Statistics 110 46:29
- Lecture 2: Story Proofs, Axioms of Probability | Statistics 110 45:40
- Lecture 3: Birthday Problem, Properties of Probability | Statistics 110 48:55
- Lecture 4: Conditional Probability | Statistics 110 49:45
- Lecture 5: Conditioning Continued, Law of Total Probability | Statistics 110 50:02
- Lecture 6: Monty Hall, Simpson's Paradox | Statistics 110 49:01
- Lecture 7: Gambler's Ruin and Random Variables | Statistics 110 51:46
- Lecture 8: Random Variables and Their Distributions | Statistics 110 50:24
- Lecture 9: Expectation, Indicator Random Variables, Linearity | Statistics 110 50:23
- Lecture 10: Expectation Continued | Statistics 110 50:10
- Lecture 11: The Poisson distribution | Statistics 110 42:46
- Lecture 12: Discrete vs. Continuous, the Uniform | Statistics 110 49:56
- Lecture 13: Normal distribution | Statistics 110 51:10
- Lecture 14: Location, Scale, and LOTUS | Statistics 110 48:55
- Lecture 15: Midterm Review | Statistics 110 38:12
- Lecture 16: Exponential Distribution | Statistics 110 18:19
- Lecture 17: Moment Generating Functions | Statistics 110 50:45
- Lecture 18: MGFs Continued | Statistics 110 49:41
- Lecture 19: Joint, Conditional, and Marginal Distributions | Statistics 110 50:09
- Lecture 20: Multinomial and Cauchy | Statistics 110 49:00
- Lecture 21: Covariance and Correlation | Statistics 110 49:26
- Lecture 22: Transformations and Convolutions | Statistics 110 47:46
- Lecture 23: Beta distribution | Statistics 110 49:48
- Lecture 24: Gamma distribution and Poisson process | Statistics 110 48:49
- Lecture 25: Order Statistics and Conditional Expectation | Statistics 110 48:15
- Lecture 26: Conditional Expectation Continued | Statistics 110 49:53
- Lecture 27: Conditional Expectation given an R.V. | Statistics 110 50:34
- Lecture 28: Inequalities | Statistics 110 47:29
- Lecture 29: Law of Large Numbers and Central Limit Theorem | Statistics 110 49:48
- Lecture 30: Chi-Square, Student-t, Multivariate Normal | Statistics 110 47:28
- Lecture 31: Markov Chains | Statistics 110 46:38
- Lecture 32: Markov Chains Continued | Statistics 110 48:24
- Lecture 33: Markov Chains Continued Further | Statistics 110 47:01
- Lecture 34: A Look Ahead | Statistics 110 36:59
- Joseph Blitzstein: "The Soul of Statistics" | Harvard Thinks Big 4 14:47
Take this course with you
Download the app to continue lessons from your phone anytime.
