MATH 320
Probability
4 Undergraduate credits
Effective
August 16, 2013
– Present
Graduation requirements this course fulfills
This is a calculus-based probability course. It covers the following topics. (1) General Probability: set notation and basic elements of probability, combinatorial probability, conditional probability and independent events, and Bayes Theorem. (2) Single-Variable Probability: binomial, geometric, hypergeometric, Poisson, uniform, exponential, gamma and normal distributions, cumulative distribution functions, mean, variance and standard deviation, moments and moment-generating functions, and Chebysheff Theorem. (3) Multi-Variable Probability: joint probability functions and joint density functions, joint cumulative distribution functions, central limit theorem, conditional and marginal probability, moments and moment-generating functions, variance, covariance and correlation, and transformations. (4) Application to problems in medical testing, insurance, political survey, social inequity, gaming, and other fields of interest.
Prerequisites
Special information
Learning outcomes
General
- Understand the theory and the applications of discrete and continuous random variables.
- Be familiar with common discrete and continuous probability distributions, and understand when and how to use them.
- Understand the theory and the applications of multivariate probability distributions, conditional expectations, and covariances.
Spring 2020
| Section | Title | Instructor | ||
|---|---|---|---|---|
| 01 | Probability | Green, Michael D | Books | Course details |