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. Undergraduate students should take the course Applied Statistics (MATH10096), which is designed specifically for them.

Course Delivery Information

Academic year 2025/26, 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		Minutes
Main Exam Diet S1 (December)	Generalised Regression Models (MATH11187)		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.