Course: STAT 34300
Title: Applied Linear Statistical Methods
Instructor(s): Rina Foygel Barber
Teaching Assistant(s): Andrew Goldstein, Enakshi Saha, Jason Willwerscheid
Class Schedule: Sec 01: TR 2:40 PM–4:00 PM in TBA
Textbook(s): Faraway, Linear Models with R, 2nd edition.
Description: This course introduces the methods and applications of fitting and interpreting multiple regression models. The underlying distributional theory is discussed briefly. Topics include the examination of residuals, the transformation of data, strategies and criteria for the selection of a regression equation, and nonlinear models; categorical input variables (factors, constraints, and design matrices); factor models; factorial design; randomization; observational units versus experimental units; typology of experiments; randomized blocks design; and categorical responses (first case, logistic regression, likelihood analysis, and some basic asymptotic properties). The course emphasizes the use and interpretation of regression analysis with the R package. Techniques discussed are illustrated by examples involving both physical and social sciences data.
Prerequisite(s): Graduate student in Statistics or Financial Mathematics or instructor consent.
Note(s): Students who need it should take Linear Algebra (STAT 24300 or equivalent) concurrently.
