Course Description
Theory of estimation and testing in full and non-full rank linear models. Normal theory distributional properties. Least squares principle and the Gauss-Markoff theorem. Estimability and properties of best linear unbiased estimators. General linear hypothesis. Application of dummy variable methods to elementary classification models for balanced and unbalanced data. Analysis of covariance. Variance components estimation for balanced data.
Spring 2024
Instructors
Meeting Patterns
Classes Start:
January 8, 2024
Classes End:
April 23, 2024
Location:
02229 SAS Hall
Class Days:
T H
Class Start Time:
11:45am
Class End Time:
1:00pm
Class Type:
Lecture
Credits:
3.00
Restrictions:
Corequisite: ST 702
