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 | Students MUST NOT also be taking
Applied Statistics (MATH10096)
|
Other requirements | Note that PGT students on School of Mathematics MSc programmes are not required to have taken pre-requisite courses, but they are advised to check that they have studied the material covered in the syllabus of each pre-requisite course before enrolling. |
Course Delivery Information
|
| Academic year 2024/25, 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
100 %,
Coursework
0 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
Coursework 0%; Examination 100%
|
| Feedback |
Not entered |
| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
|
| Main Exam Diet S1 (December) | Generalised Regression Models (MATH11187) | 2:120 | |
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
| Wood, Simon N. Generalized additive models: an introduction with R. Chapman and Hall/CRC, 2017. |
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 | Prof Patrick Rubin-Delanchy
Tel:
Email: prd1@ed.ac.uk |
Course secretary | Miss Kirstie Paterson
Tel:
Email: Kirstie.Paterson@ed.ac.uk |
|
|
University Homepage