Introductory description

This module provides incoming MSc Statistics students with a bespoke module that reviews assumed prior knowledge. It also gives these students the opportunity to engage with a UK-based education and connect with the department before they formally commence the course.

This module is not available to undergraduate students or as an unusual option.

Module web page

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

The module will provide a review of the following areas of prior knowledge.

• Probability distributions and associated theory.

• Univariate distributions,

• Joint and conditional distributions,

• Distributions of functions of random variables

• Presentation and communication of data.

• Exploratory data analysis (numerical and graphical measures)

• Use of R for exploratory elate analysis and basic statistical modelling.

Learning outcomes

By the end of the module, students should be able to:

• Evaluate probability and statistics problems and determine appropriate methods of solutions.
• Analyse problems involving probability and random variables, including univariate distributions, joint and conditional distributions and functions of random variables and apply this to solve problems.
• Apply statistical computational skills in R to a range of data and situations.

Indicative reading list

Reading lists can be found in Talis

Specific reading list for the module

Subject specific skills

• Demonstrate proficiency with probability and statistical methods.

• Create structured and coherent arguments communicating them in written form.

• Basic use of R, search for commands in help files and understand them, dealing with data

Transferable skills

• Problem solving: Use rational and logical reasoning to deduce appropriate and well-reasoned conclusions. Retain an open mind, optimistic of finding solutions, thinking laterally and creatively to look beyond the obvious. Know how to learn from failure.

• Professionalism: Prepared to operate autonomously. Aware of how to be efficient and resilient. Manage priorities and time. Self-motivated, setting and achieving goals, prioritising tasks.