en_GB
Hold Ctrl-tasten nede. Trykk på + for å forstørre eller - for å forminske.

STA600_1

Generalized linear models

This is the study programme for 2020/2021.


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. Theory for glm’s with application to regression for normally distributed data, logistic regression for binary and multinomial data; Poisson regression and survival analysis. Applications to data, principles of statistical modeling, estimation and inference are emphasized. Likelihood theory.

Learning outcome

After having completed the course one the student should:
Know the main theory for Generalized Linear Models (glm)
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, and
Be able to use glms in practical use of real data.

Contents

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. Theory for glm’s with application to regression for normally distributed data, logistic regression for binary and multinomial data; Poisson regression and survival analysis. Applications to data, principles of statistical modeling, estimation and inference are emphasized. Likelihood theory.

Required prerequisite knowledge

MAT100 Mathematical Methods 1, MAT200 Mathematical Methods 2, STA100 Probability and Statistics 1
or equivalent courses.

Recommended previous knowledge

STA500 Probability and Statistics 2

Exam

Weight Duration Marks Aid
Oral exam1/145 minutesA - FNone permitted

Coursework requirements

Two compulsory assigned exercises
Compulsory assignments must be passed for the student to have admittance to the
final course exam.

Course teacher(s)

Course coordinator
Jörn Schulz
Course teacher
Tore Selland Kleppe
Head of Department
Bjørn Henrik Auestad

Method of work

4 hours lectures and 2 hours problem solving per week.

Open to

Mathematics and Physics - Bachelor's Degree Programme
City and Regional Planning - Master of Science
Computer Science - Master's Degree Programme
Environmental Engineering - Master of Science Degree Programme
Industrial economics - Master's Degree Programme
Robot Technology and Signal Processing - Master's Degree Programme
Engineering Structures and Materials - Master's Degree Programme
Mathematics and Physics - Master of Science Degree Programme
Mathematics and Physics, 5-year integrated Master's Programme
Offshore Field Development Technology - Master's Degree Programme
Industrial Asset Management - Master's Degree Programme
Marine- and Offshore Technology - Master's Degree Programme
Offshore Technology - Master's Degree Programme
Petroleum Geosciences Engineering - Master of Science Degree Programme
Petroleum Engineering - Master of Science Degree Programme
Technical Societal Safety - Master's Degree Programme
Risk Management - Master's Degree Programme (Master i teknologi/siviling.)

Course assessment

Form and/or discussion

Literature

Literatur will be published as soon as it has been prepared by the course coordinator/teacher


This is the study programme for 2020/2021.

Sist oppdatert: 08.07.2020

History