Information in the Degree Programme Tables may still be subject to change in response to Covid-19

University Homepage
DRPS Homepage
DRPS Search
DRPS Contact
DRPS : Course Catalogue : School of Mathematics : Mathematics

Postgraduate Course: Generalised Regression Models (MATH11187)

Course Outline
SchoolSchool of Mathematics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityNot available to visiting students
SCQF Credits10 ECTS Credits5
SummaryThe 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) OR Linear Statistical Modelling (MATH10005)
Prohibited Combinations Students MUST NOT also be taking Likelihood (MATH10004) OR Likelihood and Generalized Linear Models (MATH11121)
Other requirements None
Course Delivery Information
Academic year 2021/22, 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:
  1. Demonstrate an understanding of generalised linear models and their application by solving unseen problems.
  2. Identify and apply appropriate statistical models to data and interpret the corresponding results.
  3. Use and discuss mixed effects and interpret them.
  4. 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.
Course organiserDr Bruce Worton
Tel: (0131 6)50 4884
Course secretaryMiss Gemma Aitchison
Tel: (0131 6)50 9268
Help & Information
Search DPTs and Courses
Degree Programmes
Browse DPTs
Humanities and Social Science
Science and Engineering
Medicine and Veterinary Medicine
Other Information
Combined Course Timetable
Important Information