Overview
Description
Experiments, counting techniques, probability axioms; random variables, expectation, univariate and multivariate distribution theory, measures of association, conditional probability, Bayes theorem, sequences of random variables, Tchebychev inequality, Law of Large Numbers, and Central Limit Theorem.
Units
Lecture3
Catalog Details
Offering
Offered: Every Fall and Spring
Terms
fall, spring
Attributes
Standard
Learning Outcomes
- demonstrate understanding of randomness and be able to use probability models to explain simple random phenomena. In addition, students will be able to compute summaries of probability distributions (univariate and multivariate).
- compute measures of location, dispersion, and association, as well as probability of interest for many univariate and multivariate distributions.
- assess and make use of the asymptotic results provided by the Law of Large Numbers and the Central Limit Theorem and their connection to the estimates of quantities from data.