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Harvard STAT 110: Probability →0/21
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Lecture 1: Probability and Counting | Statistics 110

46:29
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Lecture 2: Story Proofs, Axioms of Probability | Statistics 110

45:40
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Lecture 3: Birthday Problem, Properties of Probability | Statistics 110

48:54
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Lecture 4: Conditional Probability | Statistics 110

49:45
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Lecture 5: Conditioning Continued, Law of Total Probability | Statistics 110

50:01
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Lecture 6: Monty Hall, Simpson's Paradox | Statistics 110

49:00
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Lecture 7: Gambler's Ruin and Random Variables | Statistics 110

51:45
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Lecture 8: Random Variables and Their Distributions | Statistics 110

50:24
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Lecture 9: Expectation, Indicator Random Variables, Linearity (STAT 110)

50:23
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Lecture 10: Expectation Continued (STAT 110)

50:10
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Lecture 11: The Poisson Distribution (STAT 110)

42:46
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Lecture 12: Discrete vs. Continuous, the Uniform (STAT 110)

49:56
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Lecture 13: Normal Distribution (STAT 110)

51:10
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Lecture 14: Location, Scale, and LOTUS (STAT 110)

48:55
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Lecture 16: Exponential Distribution (STAT 110)

18:19
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Lecture 17: Moment Generating Functions (STAT 110)

50:45
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Lecture 18: MGFs Continued (STAT 110)

49:41
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Lecture 19: Joint, Conditional, and Marginal Distributions (STAT 110)

50:09
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Lecture 20: Multinomial and Cauchy (STAT 110)

49:00
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Lecture 21: Covariance and Correlation (STAT 110)

49:26
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Lecture 22: Transformations and Convolutions (STAT 110)

47:46

Lecture 9: Expectation, Indicator Random Variables, Linearity (STAT 110)

Joe BlitzsteinHarvard University

Professor

50:23·2013·undergraduate level

This lecture introduces expected values, exploring indicator random variables and the principle of linearity to calculate means. It also establishes the fundamental bridge between probability and expectation and presents the Geometric distribution.

MathematicsProbability & Information TheoryExpectationIndicator Random VariablesGeometric Distribution

We discuss expected values and the meaning of means, and introduce some very useful tools for finding expected values: indicator r.v.s, linearity, and symmetry. The fundamental bridge connects probability and expectation. We also introduce the Geometric distribution.

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Lecture 8: Random Variables and Their Distributions | Statistics 110

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Lecture 10: Expectation Continued (STAT 110)

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