# TMA4315 - Generalized Linear Models

### Examination arrangement

Examination arrangement: Portfolio assessment

Evaluation Weighting Duration Grade deviation Examination aids
Work 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). Portfolio assessment is the basis for the grade awarded in the course. This portfolio comprises a written final examination (70%) and works (projects) (30%). The results for the constituent parts are to be given in %-points, while the grade for the whole portfolio (course grade) is given by the letter grading system. Retake of examination may be given as an oral examination. The lectures may be given in English. If the course is taught in English, the exam may be given only in English. Students are free to choose Norwegian or English for written assessments.

• Work

### Further on evaluation

In the case that the student receives an F/Fail as a final grade after both ordinary and re-sit exam, then the student must retake the course in its entirety. Submitted work that counts towards the final grade will also have to be retaken. For more information about grading and evaluation. see «Teaching methods and activities».

### Specific conditions

Compulsory activities from previous semester may be approved by the department.

### 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 2021

Language of instruction: English, Norwegian

Location: Trondheim

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

Department of Mathematical Sciences

# Examination

#### Examination arrangement: Portfolio assessment

Term Status code Evaluation Weighting Examination aids Date Time Examination system
Autumn ORD Work 30/100
Autumn ORD School exam 70/100 2021-12-07 15:00
Summer UTS Work 30/100
Summer UTS School exam 70/100
• * 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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