Statistics 110: Probability

Statistics 110 (Probability) has been taught at Harvard University by Joe Blitzstein (Professor of the Practice in Statistics, Harvard University) each year since 2006. The on-campus Stat 110 course has grown from 80 students to over 300 students per year in that time. Lecture videos, review materials, and over 250 practice problems with detailed solutions are provided. This course is an introduction to probability as a language and set of tools for understanding statistics, science, risk, and randomness. The ideas and methods are useful in statistics, science, engineering, economics, finance, and everyday life. Topics include the following. Basics: sample spaces and events, conditioning, Bayes' Theorem. Random variables and their distributions: distributions, moment generating functions, expectation, variance, covariance, correlation, conditional expectation. Univariate distributions: Normal, t, Binomial, Negative Binomial, Poisson, Beta, Gamma. Multivariate distributions: joint, conditional, and marginal distributions, independence, transformations, Multinomial, Multivariate Normal. Limit theorems: law of large numbers, central limit theorem. Markov chains: transition probabilities, stationary distributions, reversibility, convergence. Prerequisite: single variable calculus, familiarity with matrices.

Curated by: Harvard University (35 videos)

Currently Playing: Lecture 20: Multinomial and Cauchy | Statistics 110

We introduce the Multinomial distribution, which is arguably the most important multivariate discrete distribution, and discuss its story and some of its nice properties, such as being able to "lump" categories together. We also do an example with the Cauchy distribution. Note: Around 7:39, a factor of the square root of 2 was accidentally dropped; the result should be 2 over the square root of pi, rather than the square root of 2 over pi.

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