ST515: Generalized Linear Models (5 ECTS)
STADS: 25001101Level
Bachelor course
Teaching period
The course is offered in the autumn semester.
Teacher responsible
Email: bentj@stat.sdu.dkTimetable
| Group | Type | Day | Time | Classroom | Weeks | Comment |
|---|---|---|---|---|---|---|
| Common | I | Monday | 14-16 | U145 | 36-37,40,43 | |
| Common | I | Monday | 14-16 | U49 | 39,41 | |
| Common | I | Monday | 14-16 | U49c | 44 | |
| Common | I | Thursday | 08-10 | U142 | 36-37,43 | |
| Common | I | Thursday | 08-10 | U49d | 39-41 | |
| Common | I | Thursday | 08-10 | U49 | 44 | |
| S1 | TE | Wednesday | 14-16 | U57 | 39-41,43-44 | |
| S1 | TE | Thursday | 10-12 | U145 | 40 | |
| S1 | TE | Thursday | 12-14 | U49c | 41,44 | |
| S1 | TE | Friday | 12-14 | U69a | 37 | |
| S1 | TE | Friday | 12-14 | U10 | 38,43 | |
| S1 | TE | Friday | 12-14 | U49b | 39 |
Show personal time table for this course.
Comment:
Samlæses med ST809
Prerequisites:
None
Academic preconditions:
The courses ST502 Statistical Modelling and ST519 Mathematical Statistics are recommended
Course introduction
To gain knowledge about the principles, methods and applications of generalized linear models. Participants will learn to implement and to evaluate the methods in the statistical software R.
Expected learning outcome
- reproduce key theoretical concerning elementary operations on random variables and random vectors, and to apply these to simple theoretical assignments,
- work with the concepts and models,
- understand and identify problems that can solved using generalized linear models,
- perform a practical data analysis with the techniques from the course,
- perform programming relevant to the content of the course in the statistical package used in the course,
- identify and interpret relevant information in the output and the statistical package used in the course,
- summarize the results of an analysis in a statistical report.
Natural exponential families; moment generating functions; variance functions; dispersion models; likelihood theory; chi-square, F-and t-tests; analysis of deviance; residual analysis; iterative least squares algorithm; normal linear models, logistic regression, analysis of count data, positive data.
Literature
There isn't any litterature for the course at the moment.
Website
This course uses e-learn (blackboard).
Prerequisites for participating in the exam
None
Assessment and marking:
Project assignment, pass/fail, internal evaluation by teacher. (25001102)
reexam in the same exan period or immediately thereafter.
Expected working hours
The teaching method is based on three phase model.
Intro phase: 28 hours
Skills training phase: 22 hours
Educational activities Study phase: 10 hours
Language
This course is taught in Danish or English, depending on the lecturer. However, if international students participate, the teaching language will always be English.
Course enrollment
See deadline of enrolment.
Tuition fees for single courses
See fees for single courses.
