Generalised Linear Models STATS4043

  • Academic Session: 2018-19
  • School: School of Mathematics and Statistics
  • Credits: 10
  • Level: Level 4 (SCQF level 10)
  • Typically Offered: Semester 2
  • Available to Visiting Students: Yes
  • Available to Erasmus Students: Yes

Short Description

This course introduces the class of models known as generalised linear models, providing an overview of the theory of estimation and inference as well as practical examples from various areas of applications.

Timetable

20 lectures (2 each week in Weeks 1-10 of Semester 2)

5 tutorials (fortnightly)

two 2-hour practical classes

Requirements of Entry

The normal requirement is that students should have been admitted to an Honours- or Master's-level programme in Statistics.

Excluded Courses

STATS5019 Generalised Linear Models (Level M)

STATS3TBC Statistics 3G: Generalised Linear Models

Assessment

90-minute, end-of-course examination (100%)

Main Assessment In: April/May

Are reassessment opportunities available for all summative assessments? Not applicable

Reassessments are normally available for all courses, except those which contribute to the Honours classification. For non Honours courses, students are offered reassessment in all or any of the components of assessment if the satisfactory (threshold) grade for the overall course is not achieved at the first attempt. This is normally grade D3 for undergraduate students and grade C3 for postgraduate students. Exceptionally it may not be possible to offer reassessment of some coursework items, in which case the mark achieved at the first attempt will be counted towards the final course grade. Any such exceptions for this course are described below. 

Course Aims

■ To provide an overview of linear statistical models and their generalizations;

■ To acquaint students with the theory of generalized linear models;

■ To provide practical examples from various areas of applications.

Intended Learning Outcomes of Course

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

■ use models with various link functions and link distributions such as models for discrete data;

■ perform binary regression and analysis of contingency tables;

■ apply log-linear models;

■ analyse a given set of data using generalised linear models;

■ sketch key aspects of the theory of generalised linear models.

Minimum Requirement for Award of Credits

None