Teaching modes (remote, hybrid, in-person) are subject to change at any point. If this happens, faculty will be in contact with students enrolled in their courses.
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This course explores modern statistical methods for drawing scientific inferences from longitudinal data, i.e., data collected repeatedly on experimental units over time. The independence assumption made for most classical statistical methods does not hold with this data structure because we have multiple measurements on each individual. Topics will include linear and generalized linear models for correlated data, including marginal and random effect models, as well as computational issues and methods for fitting these models. We will consider many applications in the social and biological sciences.
Format: lecture; Hybrid format. Approximately 2/3 of class time will be lecture (in person for students who are on campus, recorded for remote students). All synchronous students (whether in person or online) will attend a remote lab/discussion section each week. Asynchronous options will be provided for students unable to participate synchronously.
Grading: yes pass/fail option,
yes fifth course option
performance on exams, homework, and a project
STAT 201 and STAT 346
junior and senior Statistics majors
The course will cover a variety of statistical analysis methods for longitudinal data.