Course Description
Estimation and testing in full and non-full rank linear models. Normal theory distributional properties. Least squares principle and the Gauss-Markov theorem. Estimability, analysis of variance and co variance in a unified manner. Practical model-building in linear regression including residual analysis, regression diagnostics, and variable selection. Emphasis on use of the computer to apply methods with data sets. Credit not given for both ST 705 and ST 503. Note: this course will be offered in person (Spring) and online (Summer).
Spring 2024
Instructors
Meeting Patterns
Classes Start:
January 8, 2024
Classes End:
April 23, 2024
Location:
05270 SAS Hall
Class Days:
T H
Class Start Time:
1:30pm
Class End Time:
2:45pm
Class Type:
Lecture
Credits:
3.00
Restrictions:
P: ST 501 and MA 405 or equivalent (Linear Algebra); C: ST 502 P: Graduate Standing
