Study Abroad & Exchange Programmes at Glasgow

This course introduces students to statistical modelling, in particular linear models, and demonstrates the importance and usefulness of modelling in real applications.

Lectures: Tuesday and Thursday at 9.00 am.

Labs/workshops and drop-in help rooms arranged via MyCampus (several groups available).

Required: Mathematics 1 at grade D or better.

Strongly recommended: Statistics 1Y and Statistics 1Z.

Statistics 2R: Probability

Statistics 2S: Statistical Methods

Mathematics 2A

Mathematics 2B

Mathematics 2D

End-of-course examination (80%); coursework (20%).

Reassessment will, generally, not be available for the coursework.

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. 

Reassessment will, generally, not be available for the coursework component of this course.

The aims of this course are:

■ to introduce students to statistical modelling, in particular linear models;

■ to demonstrate the importance and usefulness of modelling in real applications;

■ to equip students to apply regression modelling to solve problems from a wide range of disciplines;

■ to train students to communicate the results of their analyses in clear non-technical language;

■ to train students to use computers appropriately for statistical analysis;

■ to promote an interest in Statistics and encourage students to study more advanced courses.

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

■ explain what is meant by a statistical model in general and a linear model in particular;

■ estimate and make inferences about the population correlation coefficient;

■ derive estimators for parameters in simple models using least squares and maximum likelihood;

■ formulate linear models in vector-matrix notation and apply general results to derive least squares estimators in particular contexts;

■ calculate and comment on R2 and assess the assumptions of a linear model using residual plots;

■ obtain and interpret confidence and prediction intervals for linear models;

■ use interval estimates, hypothesis tests and R2 for model selection and model comparison;

■ explain how the linear model can be extended.

Minimum requirement as in code of assessment