Introductory description

This module provides the opportunity for students outside the Department of Statistics to develop their statistical programming and modelling skills.

It will provide background required for other statistics modules offered by the department such as ST240 Linear Statistical Modelling.

Availability. This module is not available to students within the Department of Statistics, who take ST117 instead. This module is available for external students who have the necessary pre-requisite knowledge.

Pre-requisites. ST120 Introduction to Probability.

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.

Introduction to R
Exploratory data analysis: methods of visualisation and summary statistics
Sampling from standard discrete and continuous distributions (Bernoulli, Geometric, Poisson, Gaussian, Gamma)
Generic methods for sampling from univariate distributions
The use of R to illustrate probabilistic notions such as conditioning, convolutions and the law of large numbers
Examples of modelling real data (but without formal statistical inference) and the use of visualisations to assess fit

Learning outcomes

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

• have familiarity with the R software package, making use of it for exploratory data analysis.
• use R to simulate samples from a variety of probability distributions.
• propose appropriate probabilistic models for simple data sets.
• describe, fit and interpret a simple linear regression model.

Indicative reading list

Reading lists can be found in Talis

Specific reading list for the module

Subject specific skills

• Select and apply appropriate mathematical and/or statistical techniques.
• Create structured and coherent arguments communicating them in written form.
• Communicate subject-specific information effectively and coherently.
• Analyse problems, abstracting their essential information formulating them using appropriate mathematical language to facilitate their solution.
• Select and apply appropriate statistical programming language (for example, R) for exploratory data analysis.

Transferable skills

• Critical thinking: extracting patterns from incomplete data and using them to form evidence-based conclusions.
• Problem solving: use of logical reasoning to build arguments grounded in evidence and with explicit underlying assumptions.
• Self-awareness: monitoring of your own learning and seeking feedback.
• Communication: verbal discussion of ideas in seminars and among peers; written communication in assignments.
• Information literacy: evaluation of data and uncertainty in a model-based way.
• Digital literacy: use of computational tools to understand and visualise data, and to produce reports.
• Professionalism: self-motivation, taking charge of your own learning, and prioritising effectively.