|Title:||Applied Linear Statistical Methods|
|Instructor(s):||Rina Foygel Barber|
|Teaching Assistant(s):||Andrew Goldstein, Lei Sun, and Fan Yang|
|Class Schedule:||Sec 01: TR 11:00 AM–12:20 PM in Harper Memorial 140|
|Textbook(s):||Faraway, Linear Models with R, 1st edition.|
|Description:||This course introduces the theory, methods, and applications of fitting and interpreting multiple regression models. Topics include the examination of residuals, the transformation of data, strategies and criteria for the selection of a regression equation, nonlinear models, biases due to excluded variables and measurement error, and the use and interpretation of computer package regression programs. The theoretical basis of the methods, the relation to linear algebra, and the effects of violations of assumptions are studied. Techniques discussed are illustrated by examples involving both physical and social sciences data.
Prerequisite(s): STAT 24500 or equivalent, and linear algebra (STAT 24300 or equivalent)
Note(s): The prerequisites are under review and may change.