Statistics 110 (Probability), which 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.
Statistics 110: Probability Harvard iTunes U Course Professor: Joe Blitzstein, Professor of the Practice, Harvard Statistics Department Online: http://stat110.net and http://twitter.com/stat110 Book: Introduction to Probability Prerequisites: single-variable calculus, familiarity with matrices. Description: A comprehensive introduction to probability, as a language and set of tools for understanding statistics, science, risk, and randomness. Basics: sample spaces and events, con-ditional probability, and Bayes Theorem. Univariate distributions: density functions, expectation and variance, Normal, t , Binomial, Negative Binomial, Poisson, Beta, and Gamma distributions. Multivariate distributions: joint and conditional distributions, independence, transformations, and Multivariate Normal. Limit laws: law of large numbers, central limit theorem. Markov chains: transition probabilities, stationary distributions, convergence. Shorter Description: The world is replete with randomness and uncertainty; probability and statistics extend logic into this realm. We will systematically introduce the ideas and tools of probability, which are useful in statistics, science, philosophy, engineering, economics, ﬁnance, and everyday life. Both the mathematical results of the subject and applications to solving problems will be studied, with examples ranging from gambling to genetics. Even Shorter Description: How to understand and work with randomness and uncertainty through probability models, random variables and their distributions, and thinking conditionally.