(Offered as STAT 360 and MATH 360) This course explores the nature of probability and its use in modeling real world phenomena. There are two explicit complementary goals: to explore probability theory and its use in applied settings, and to learn parallel analytic and empirical problem-solving skills. The course begins with the development of an intuitive feel for probabilistic thinking, based on the simple yet subtle idea of counting. It then evolves toward the rigorous study of discrete and continuous probability spaces, independence, conditional probability, expectation, and variance. Distributions covered include the binomial, hypergeometric, Poisson, normal, Gamma, Beta, multinomial, and bivariate normal. Other topics include generating functions, order statistics, and limit theorems.
Students will engage with the material through synchronous lectures, individual and team based learning activities, and office hours. In addition, some lecture videos and discussion boards will be made available on Moodle for asynchronous engagement.
Requisite: MATH 121 or consent of the instructor. Limited to 24 students. Fall semester. Professor Donges.
If Overenrolled: For the Fall, priority for rising sophomores and Statistics majors, then Mathematics majors.