Postgraduate Course: Generalised Regression Models (MATH11187)
Course Outline
| School | School of Mathematics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | The course builds on the material covered in MATH10095 Statistical Methodology, extending the statistical techniques described to generalised linear models. |
| Course description |
Topics to be covered include :
- generalised linear models;
- analysis of deviance;
- exponential families; and
- generalised linear mixed models.
|
Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
Students MUST have passed:
Statistical Methodology (MATH10095)
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Course Delivery Information
|
| Academic year 2022/23, Not available to visiting students (SS1)
|
Quota: None |
| Course Start |
Semester 1 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 22,
Seminar/Tutorial Hours 5,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
69 )
|
| Assessment (Further Info) |
Written Exam
95 %,
Coursework
5 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
Coursework 5%; Examination 95%
An assignment in mid-semester in which students identify and apply appropriate statistical models to data and interpret the corresponding results. This will be based on the material from Workshops/Classes. |
| Feedback |
Students would get feedback from the tutors as part of the online workshops, in breakout rooms. I will ask students to prepare material before the classes. I would expect to use the breakout rooms in Zoom etc, but other possibilities will hopefully be available by January 2021. In addition for the assignment work students will be able to get feedback on their progress before submission, as well as mid-semester feedback on the marked work. |
| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
|
| Main Exam Diet S1 (December) | | 2:00 | |
Learning Outcomes
On completion of this course, the student will be able to:
- Demonstrate an understanding of generalised linear models and their application by solving unseen problems.
- Identify and apply appropriate statistical models to data and interpret the corresponding results.
- Use and discuss mixed effects and interpret them.
- Use R to fit generalised linear models to data.
|
Reading List
| An Introduction to Generalized Linear Models. Dobson and Barnett. Chapman and Hall. |
Additional Information
| Graduate Attributes and Skills |
Not entered |
| Special Arrangements |
These Postgraduate Taught courses may be taken by Undergraduate students *without* requiring a concession (NB. students on Postgraduate taught programmes are given priority in the allocation of places). For all other Postgraduate Taught courses the student and/or Personal Tutor must seek a concession. |
| Keywords | GRM,Regression,Statistics |
Contacts
| Course organiser | Dr Bruce Worton
Tel: (0131 6)50 4884
Email: Bruce.Worton@ed.ac.uk |
Course secretary | Miss Gemma Aitchison
Tel: (0131 6)50 9268
Email: Gemma.Aitchison@ed.ac.uk |
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