Stat 240: Discrete Probability
Content is available on the course Learning Suite webpage.
Course Description and Learning Outcomes
This course serves as a mathematical introduction to discrete probability. Topics include set theory and operations, discrete sample space, counting techniques, probability of events and finite sample spaces, discrete random variables, expected value and variance, and named discrete distributions, including binomial, Poisson, and geometric.
- Set Theory and Basic Set Operations: apply fundamentals of set theory and basic set operations
- Discrete Sample Space: enumerate the elements of a discrete sample space
- Counting Techniques: demonstrate familiarity with counting techniques--sampling with order, without order, binomial coefficients
- Probability Problems in Finite Sample Spaces: solve probability problems in finite sample spaces
- Solve Problems: solve problems using axioms of probability, conditional probability, independence, and Bayes theorem
- Discrete Random Variables: calculate expected values and variances of discrete random variables
- Discrete Distributions: understand the assumptions and properties of the named discrete distributions (Bernoulli, binomial, Poisson, geometric)
- Solve Problems: solve problems with the pdf, cdf, moments of discrete univariate random variables