This course introduces the theory of statistical inference: given a data set, how do we estimate the parameters of probabilistic models like those introduced in MATH 220/STAT 220? What is the optimal way to make use of the information in our data? Topics include the theories that underlie traditional hypothesis testing and confidence intervals, such as maximum likelihood inference and sufficiency. The course will also cover Bayesian techniques for point and interval estimation and resampling approaches, such as the bootstrap.

Units: 1

Max Enrollment: 24

Prerequisites: MATH 220/STAT 220.

Instructor: Tannenhauser

Distribution Requirements: MM - Mathematical Modeling and Problem Solving

Typical Periods Offered: Every other year

Semesters Offered this Academic Year: Spring

Notes: