2024/25 Taught Postgraduate Module Catalogue

OGDS5101M Statistical Methods

15 creditsClass Size: 150

Module manager: Dr Leonid Bogachev
Email: l.v.bogachev@leeds.ac.uk

Taught: 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

Pre-requisite qualifications

Students are required to meet the programme entry requirements prior to studying the module.

This module is mutually exclusive with

OMAT5101M

Statistical Methods

Module replaces

None

This module is not approved as an Elective

Module summary

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.

Objectives

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.

Learning outcomes
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.

Skills outcomes
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.

Syllabus

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.

Teaching methods

Delivery type	Number	Length hours	Student hours
Discussion forum	6	2.00	12.00
WEBINAR	1	1.50	1.50
WEBINAR	5	1.00	5.00
Independent online learning hours			42.00
Private study hours			89.50
Total Contact hours			18.50
Total hours (100hr per 10 credits)			150.00

Private study

Across each week of learning students will actively engage with pre-prepared teaching and learning resources which scaffold learners to achieve learning outcomes (independent online learning). Each week follows a set pattern of acquiring knowledge which is then applied to a substantive activity which will usually be authentic to real-world application. Weekly asynchronous discussions (such as discussion boards) allow for peer-to-peer and peer-to-tutor discussion which supports completion of the substantive activity. At the end of each week of learning students consolidate their learning through reflective activities and a weekly live webinar session with the module tutor. Each unit also provides students with the opportunity for exploration and self-directed learning as is expected at masters level (private study).

Opportunities for Formative Feedback

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.

Methods of assessment

Coursework

Assessment type	Notes	% of formal assessment
In-course Assessment	Students will be tested predominantly using e-assessment methods or MCQs.	20.00
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.	80.00
Total percentage (Assessment Coursework)		100.00

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.

Reading list

There is no reading list for this module

Last updated: 18/11/2024

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