Introductory description

This module is an introduction to statistical thinking, modelling, and inference. At its core are the concepts of a statistical model and the associated likelihood function, as well as their manipulation to obtain rigorous inferences.

This module is core for students with their home department in Statistics and available to students from other departments for whom it is a listed option. It will be useful for all subsequent modules on statistics.

This module is NOT available as an unusual option. Students from outside the Statistics dept who are interested in a first year statistics module should consider taking ST121 Statistical Laboratory.

Pre-requisites:
Statistics students: ST118 Probability 1
Non-Statistics students: 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.

This module introduces the inherently interdisciplinary field of modern statistics. It covers the specification of appropriate statistical models for a variety of data sets, the mathematical underpinnings of model-based statistical inference, and the interpretation and communication of inference outcomes. The R computational language is introduced and used as a toolkit for examples throughout.

Learning outcomes

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

• Describe an appropriate probabilistic model and associated likelihood function for a simple data set.
• Calculate estimates of unknown parameters and their associated uncertainty based on a simple model and observed data.
• Compare model predictions and observed data graphically.
• Describe the modelling assumptions underlying a simple statistical model.
• Interpret model output to inform decisions or further experiments.
• Know the R programming environment well enough to write simple scripts to accomplish computational or data visualisation tasks.
• Discuss ethical aspects of a data collection/selection, their statistical analysis, and the interpretation and communication of the results.
• Apply team working processes to integrate ideas from various team members, break complex tasks into parts, and critically appraise each others contributions.

Indicative reading list

See Talis Aspire link.

View reading list on Talis Aspire

Interdisciplinary

The module includes dataset analysis arising from a range of areas. Studying these datasets requires utilising statistical knowledge and the ability to communicate outcomes across multiple domains and discplines.

Subject specific skills

• Select and apply appropriate mathematical and/or statistical techniques.
• Create structured and coherent arguments communicating them in written form.
• Construct and develop logical mathematical arguments with clear identification of assumptions and conclusions.
• 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.
• Understand major aspects of data collection, generation, and quality, and how this influences analyses and conclusions.

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.
• Teamwork: collaboration with with members of your team in seminars, during self-study and during the completion of extended tasks.
• Communication: verbal discussion of ideas in seminars and among peers; written communication in assignments and the final project with members of your team.
• 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.
• Ethics: reflect on professional responsibilities as a statistician in conjunction with the generation and dissemination of information.
• Time management: dividing a long-term project into distinct phases and employing project management techniques to ensure timely delivery.