Study Abroad & Exchange Programmes at Glasgow

This course introduces students to two key concepts in Statistics: likelihood inference and testing in parametric statistical models.

Lectures: Course taught in the last two thirds of the semester (Monday to Thursday at 9.00 am with fewer lectures at the beginning)
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

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 key formal concepts used in Statistics such as hypothesis testing in Statistical models and likelihood-based point estimation and interval estimation;

■ to equip students to apply statistical ideas to solve problems from a wide range of disciplines;

■ to introduce students to a statistical computing package;

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

■ 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:

■ describe and illustrate data using suitable summary statistics and plots;

■ define and derive properties of point estimates such as the bias and the variance;

■ apply the principle of maximum likelihood to obtain point and interval estimates of parameters in simple statistical models;

■ formulate hypothesis tests in some common contexts, correctly using the terms null hypothesis, alternative hypothesis, test statistic, rejection region, significance level, power, p-value;

■ carry out a range of common hypothesis tests, with due regard to the underlying assumptions;

■ carry out tests of goodness-of-fit, association and independence;

■ write statistical conclusions clearly.

Minimum requirement as  in code of assessment