course-details-portlet

TMA4315 - Generalized Linear Models

About

Examination arrangement

Examination arrangement: Aggregate score
Grade: Letter grades

Evaluation Weighting Duration Grade deviation Examination aids
Project 30/100
School exam 70/100 4 hours C

Course content

Univariate exponential family. Multiple linear regression. Logistic regression. Poisson regression. General formulation for generalised linear models with canonical link. Likelihood-based inference with score function and expected Fisher information. Deviance. AIC. Wald, score and likelihood-ratio test. Linear mixed effects models with random components of general structure. Random intercept and random slope. Generalised linear mixed effects models. Strong emphasis on programming in R. Possible extensions: quasi-likelihood, over-dispersion, models for multinomial data, analysis of contingency tables, quantile regression.

Learning outcome

1. Knowledge. The student can assess whether a generalised linear model can be used in a given situation and can further carry out and evaluate such a statistical analysis. The student has substantial theoretical knowledge of generalised linear models and associated inference and evaluation methods. This includes regression models for normal data, logistic regression for binary data and Poisson regression. The student has theoretical knowledge about linear mixed models and generalised linear mixed effects models, both concerning model assumptions, inference and evaluation of the models. Main emphasis is on normal, binomial and Poisson models with random intercept and random slope. 2. Skills. The student can assess whether a generalised linear model or a generalised linear mixed model can be used in a given situation, and can further carry out and evaluate such a statistical analysis.

Learning methods and activities

Lectures, exercises and works (projects).

Further on evaluation

Retake of examination may be given as an oral examination. The re-take exam will be held in August.

Students are free to choose Norwegian or English for written assessments.

Students that want to improve their grade in the course, can choose to retake one of the two evaluations. If the evaluation is changed, the whole course must be retaken.

Course materials

Will be announced at the beginning of the semester.

More on the course

Facts

Version: 1
Credits:  7.5 SP
Study level: Second degree level

Coursework

Term no.: 1
Teaching semester:  AUTUMN 2024

Language of instruction: English

Location: Trondheim

Subject area(s)
  • Statistics
  • Technological subjects
Contact information
Course coordinator:

Department with academic responsibility
Department of Mathematical Sciences

Timetable

List view
Calendar view

Examination

Examination arrangement: Aggregate score

Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
Autumn ORD School exam 70/100 C 2024-11-27 09:00 INSPERA
Room Building Number of candidates
SL274 Sluppenvegen 14 2
SL228 Sluppenvegen 14 1
SL311 orange sone Sluppenvegen 14 28
SL310 hvit sone Sluppenvegen 14 2
SL271 Sluppenvegen 14 1
Autumn ORD Project 30/100

Submission
2024-11-20


23:59

INSPERA
Room Building Number of candidates
Summer UTS School exam 70/100 C INSPERA
Room Building Number of candidates
  • * The location (room) for a written examination is published 3 days before examination date. If more than one room is listed, you will find your room at Studentweb.
Examination

For more information regarding registration for examination and examination procedures, see "Innsida - Exams"

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