# Course Catalogue

Please note: there may be some adjustments to the teaching arrangements published in the course catalogue for 2020-21. Given current circumstances related to the Covid-19 pandemic it is anticipated that some usual arrangements for teaching on campus will be modified to ensure the safety and wellbeing of students and staff on campus; further adjustments may also be necessary, or beneficial, during the course of the academic year as national requirements relating to management of the pandemic are revised.

## Statistics 3I: Inference STATS3015

• Academic Session: 2021-22
• School: School of Mathematics and Statistics
• Credits: 10
• Level: Level 3 (SCQF level 9)
• Typically Offered: Semester 1
• Available to Visiting Students: Yes
• Available to Erasmus Students: Yes

### Short Description

To introduce students to the fundamental principles of likelihood-based inference, with emphasis on the large sample results that are widely used in practice.

### Timetable

Lectures: 2 hours per week (at times to be arranged)

Tutorials: fortnightly (at times to be arranged)

### Requirements of Entry

The normal requirement is that students should have been admitted to the third year of a Designated Degree programme in Statistics.

### Excluded Courses

Inference 3 [STATS4012]

Statistical Inference (Level M) [STATS5028]

### Co-requisites

The courses prescribed in the Designated Degree programme to which the student has been admitted.

### Assessment

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

Main Assessment In: April/May

### Course Aims

The aim of this course is:

■ To introduce students to the fundamental principles of likelihood-based inference, with emphasis on the large sample results that are widely used in practice.

### Intended Learning Outcomes of Course

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

■ write down the likelihood, given a description of a model;

■ perform likelihood based inference for a variety of simple statistical models;

■ maximise likelihoods for simple models numerically;

■ perform interval estimation and perform hypothesis tests for parameters in simple models;

■ sketch what constitute the 'good' properties of estimation, testing and interval estimation procedures;

■ apply the bootstrap technique to practical problems where information on the variability of estimates is required.

### 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.