STAA 552 - Generalized Regression Models

  • 2 credits

Categorical data analysis, estimation and testing for contingency tables, introduction to generalized linear models, logit and probit models for binary regression, extensions to nominal and ordinal multicategory responses, count data, Poisson and negative binomial regression, log-linear models.

This course has print-based exams that require a proctor. A Distance Proctor Form will be required. Electronic proctoring is not available for this.

This course can be applied towards:

Prerequisite

STAA 551 (Regression Models and Applications or concurrent registration) or STAT 540 (Data Analysis and Regression) or written consent of instructor. This is a partial-semester course.

Important Information

Tuition includes access to lecture recordings which are available by streamed video. Lecture recordings may also be available by download or on DVD. To determine viewing options, contact the Department of Statistics degree program staff at stats_ddp@mail.colostate.edu. Visit the Department of Statistics website to learn more about what to do after registration, including creating your eID (if necessary) and accessing your course.

Textbook and Materials

Visit the Department of Statistics' website for textbook information.

Instructors

F. Jay Breidt
F. Jay Breidt

(970) 491-6786 | fjay.breidt@colostate.edu

F. Jay Breidt is professor and past chair in the Department of Statistics at Colorado State University. Dr. Breidt joined the Colorado State faculty in 2001 after serving on the faculty in the Department of Statistics at Iowa State University since 1991. His research interests include time series, environmental monitoring, and survey sampling. He is a fellow of the American Statistical Association, elected member of the International Statistical Institute, and winner of the 2004 Distinguished Achievement Award from the American Statistical Association (ASA) Section on Statistics and the Environment. He is a member of the Federal Economic Statistics Advisory Committee and regularly serves on panels for the National Academies of Science. He received his M.S.and Ph.D. in statistics at Colorado State.

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