MATH 320


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.


Special information

Note: Students whose prerequisites are not identified by the system would contact the Math and Statistics Department for an override at First day attendance required except by instructor permission.

Learning outcomes


  • 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.