Generalized Linear Models | University of Stavanger

Introduction to glm, which is a generalization of (multiple) regression for normally distributed responses to responses from a larger class of distributions, especially discrete responses.

Content

Theory for GLMs with application to among other tings, regression for normally distributed data, logistic regression for binary and multinomial data; Poisson regression and survival analysis. Principles of statistical modeling, likelihood theory, estimation and inference, bayesian methods. Applications and analyses of data sets are emphasized.

Learning outcome

After having completed the course one the student should:

Know the main theory for generalized linear models
Know how regression with binary, multinomial, Poisson- and survival time responses may be done
Understand use of likelihood estimation generally and especially for generalized linear models
Be able to apply the theory in practical use on real data.

Required prerequisite knowledge

Mathematical Methods 1 (MAT100)
Mathematical Methods 2 (MAT200)
Probability and Statistics 1 (STA100)
Probability and Statistics 2 (STA500)

Mathematical Methods 1 (MAT100)
Linear Algebra (MAT110)
Probability and Statistics 1 (STA100)
Probability and Statistics 2 (STA500)

Mathematical Methods 1 (MAT100)
Mathematical Methods 2 (MAT200)
Probability and Statistics 1 (STA100)
Statistical Learning (STA530)

Mathematical Methods 1 (MAT100)
Linear Algebra (MAT110)
Probability and Statistics 1 (STA100)
Statistical Learning (STA530)

or equivalent courses.

Recommended prerequisites

Probability and Statistics 2 (STA500)

Exam

Oral exam

Weight 1/1

Duration 45 Minutes

Marks Letter grades

Aid None permitted

Oral exam is individual. English or Norwegian language may be used by the candidate.

Coursework requirements

Two compulsory assigned exercises

2 mandatory assignments must be approved for the student to have access to the exam.

Method of work

4 hours lectures and 2 hours problem solving per week. Teaching language is English.

Open for

Admission to Single Courses at Master Level at the Faculty of Science and Technology

City and Regional Planning Computational Engineering Computer Science Environmental Engineering Industrial Economics Structural and Mechanical Engineering Mathematics and Physics Mathematics and Physics Industrial Asset Management Marine and Offshore Technology Petroleum Engineering

Admission requirements

Must meet the admission requirements of one of the study programmes the course is open for.

Course assessment

The faculty decides whether early dialogue will be held in all courses or in selected groups of courses. The aim is to collect student feedback for improvements during the semester. In addition, a digital course evaluation must be conducted at least every three years to gather students’ experiences.

Contact

Head of Department:

Bjørn Henrik Auestad

Course coordinator:

Jörn Schulz

Offered by

Faculty of Science and Technology

Department of Mathematics and Physics

The course description is retrieved from FS (Felles studentsystem). Version 1

Generalized Linear Models (STA600)

Facts

Introduction