Linear and Generalized Linear Models
STAT 244
Subject & Catalog Number
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Course Information
Description
The theory and application of linear and generalized linear models, including linear models for normal responses, least-squares theory and inference, models for binary and multinomial data, log-linear models for count data, overdispersion and quasi-likelihood methods, model selection, and computational issues. Emphasizes the theory and development of methodology.
Recommended Prep
Strong statistics background required (at the second-year graduate level), Statistics 210 may be taken concurrently, Statistics 211 desirable.
School
Faculty of Arts & Sciences
Credits
4
Cross Reg
Available for Harvard Cross Registration
Department
Statistics
Course Component
Lecture
NOTE: This course requires additional sections; you will be prompted to choose secondary components during the Add to Cart process
General Education
N/A
Quantitative Reasoning with Data
N/A
Divisional Distribution
Science & Engineering & Applied Science
Course Level
Primarily for Graduate Students
