Postgraduate taught 

Data Analytics MSc

Advanced Predictive Models STATS5098

  • Academic Session: 2025-26
  • School: School of Mathematics and Statistics
  • Credits: 10
  • Level: Level 5 (SCQF level 11)
  • Typically Offered: Semester 2
  • Available to Visiting Students: No
  • Collaborative Online International Learning: No

Short Description

This course is concerned with models which can account for a non-normal distribution of the response and/or a non-parametric relationship between variables and/or the fact that data is not independent but correlated.

Timetable

20 lectures (2 each week)

4 2-hour computer-based practicals

Excluded Courses

Advanced Predictive Models (ODL)

Generalised Linear Models

Statistics 3G: Generalised Linear Models

Assessment

20% set exercise - continuous assessment, for example via online quizzes

80% final exam - Degree exam in the exam diet

Main Assessment In: April/May

Course Aims

■ to acquaint students with the theory of exponential families;

■ to introduce generalised linear models;

■ to introduce generalised additive models

■ to introduce models for correlated and grouped data, e.g mixed models

Intended Learning Outcomes of Course

By the end of this course students will be able to:

■ explain and/or derive key aspects of the theory of exponential families and several classes of models.

■ interpret models with various link functions and link distributions

■ select and apply appropriate statistical models to different data structures

Minimum Requirement for Award of Credits

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment