View Specification DocumentRegression Models (DD80) STATS3016
- Academic Session: 2026-27
- School: School of Mathematics and Statistics
- Credits: 20
- Level: Level 3 (SCQF level 9)
- Typically Offered: Runs Throughout Semesters 1 and 2
- Available to Visiting Students: Yes
- Collaborative Online International Learning: No
- Curriculum For Life: No
Short Description
This course extends previous work on the normal linear model and introduces the class of generalised linear models (GLM). For these classes of models the course covers model specification, estimation, inference, model building and model checking. It illustrates these concepts with practical applications across various domains.
Timetable
40 lectures (2 each week in Weeks 1-10 of Semesters 1 and 2).
8 1-hour tutorials (4 each in Semesters 1 and 2).
7 2-hour labs (3 each in Semesters 1 and 2, plus 2h lab for assessment in Semester 1).
Requirements of Entry
The normal requirement is that students should have been admitted to the third year of the Designated Degree programme in Statistics.
Excluded Courses
Regression Models (Level M) [STATS5025]
Regression Models [STATS4015]
Co-requisites
The courses prescribed in the Designated Degree programme to which the student has been admitted.
Assessment
Two pieces of continuous assessment (one in each semester) - 20% (each piece worth 10%). End of course examination lasting 120 minutes - 80%.
Main Assessment In: April/May
Course Aims
The aims of this course are:
■ to introduce the theory and methodology of normal linear models and generalised linear models.
■ to describe the main approaches for specifying, building, evaluating, and making inferences from these models.
■ to describe specific models within this wider class, including analysis of variance, multiple regression, logistic regression and models for count data;
■ to illustrate how to fit these models to real data sets from various application areas.
Intended Learning Outcomes of Course
By the end of this course students will be able to:
■ Formulate statistically valid regression models for different kinds of data using distributions from the exponential family.
■ Build and fit models to real data using statistical software and interpret the results.
■ Perform statistical inference using the fitted models with reference to distributional theories
■ Compare a range of competing models using appropriate model comparison techniques.
■ Assess the validity of the assumptions underpinning these models for real data set