Course - Generalized Linear Models - TMA4315
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). The lectures may be given in English.
Further on evaluation
Retake of examination may be given as an oral examination.
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.
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 evaluation must be retaken.
Recommended previous knowledge
TMA4267 Linear Statistical Models or TMA4255 Applied Statistics. Good knowledge in R, a software environment for statistical computing and graphics.
Course materials
Will be announced at the beginning of the semester.
Version: 1
Credits:
7.5 SP
Study level: Second degree level
Term no.: 1
Teaching semester: AUTUMN 2023
Language of instruction: English, Norwegian
Location: Trondheim
- Statistics
- Technological subjects
Department with academic responsibility
Department of Mathematical Sciences
Examination
Examination arrangement: Aggregate score
- Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
- Autumn ORD School exam 70/100 C 2023-12-05 15:00 INSPERA
-
Room Building Number of candidates SL321 Sluppenvegen 14 1 SL311 grønn sone Sluppenvegen 14 32 SL515 Sluppenvegen 14 2 SL310 turkis sone Sluppenvegen 14 1 -
Autumn
ORD
Project
30/100
Submission
2023-11-06
INSPERA
14:00 -
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.
For more information regarding registration for examination and examination procedures, see "Innsida - Exams"