Module manager: Dr Leonid Bogachev
Email: l.v.bogachev@leeds.ac.uk
Taught: Semester 1 May to 30 Jun (2mth)(adv yr), 1 May to 30 June, 1 Nov to 31 Dec, 1 Nov to 31 Dec (2mth)(adv yr) View Timetable
Year running 2024/25
Students are required to meet the programme entry requirements prior to studying the module.
N/A
This module is not approved as an Elective
The module provides a general introduction to statistical thinking and data analysis including probability rules and distributions, methods of estimation and hypotheses testing and present the basics of Bayesian inference.
Through a combination of theory and examples-based practice, the material in the module will allow students to calculate statistical estimates and understand the uncertainty in those estimates as well as select, apply and interpret the outcome of statistical tests which are widely used in a range of applications.
On completion of this module students will be able to:
1. Understand and be able to explain the role of statistical models.
2. Be able to compute appropriate statistical estimates and to assess the estimation error.
3. Perform a range of statistical tests.
4. Interpret the results of statistical tests.
5. Understand the interplay of prior information and data in Bayesian inference.
The module will develop the following skills:
- Students will learn to carry out the statistical analysis described above using appropriate software.
- Making judgements based on statistical analysis.
1. The role of statistical models.
2. Probability rules and distributions.
3. Statistical estimators, bias, mean squared error (MSE).
4. Standard examples of estimators (e.g. sample mean, sample variance).
5. Statistical tests, types of error and error probabilities.
6. Examples of tests (such as z-test and t-test).
7. Computing estimates and performing tests in R.
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| On-line Learning | 1 | 1.5 | 1.5 |
| On-line Learning | 5 | 1 | 5 |
| Discussion forum | 6 | 2 | 12 |
| Independent online learning hours | 42 | ||
| Private study hours | 89.5 | ||
| Total Contact hours | 18.5 | ||
| Total hours (100hr per 10 credits) | 150 | ||
Students will have weekly formative assignments (e.g. quizzes, problem sheets or practical tasks) for each taught unit of the module and will be given model solutions with comments.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| In-course Assessment | Students will be tested predominantly using e-assessment methods or MCQs. | 20 |
| Assignment | The assignment will require students to complete a written report which may feature components of R code, R outputs, calculations and critical analysis of results. It is expected that the assignment will be completed in one week. | 80 |
| Total percentage (Assessment Coursework) | 100 | |
It is expected that the students resitting will do so via the assignment only which can cover all learning outcomes. This will be available during the next running of the module.
There is no reading list for this module
Last updated: 5/24/2024
Errors, omissions, failed links etc should be notified to the Catalogue Team