Statistics and Data Analysis module (MA22004)

Data is everywhere, from scientific research and healthcare to finance, business, sport, and public policy. In this module, you will learn how statistics can turn data into evidence, helping people make better decisions in research, industry, and society. You will explore patterns, test ideas, and draw reliable conclusions from sample data.

Building on MA12003 Statistics and Probability, this module develops your skills in statistical inference and data analysis. You will learn how samples can be used to estimate unknown values in a wider population, how to measure uncertainty, and how to judge whether results are likely to be meaningful.

A major part of the module is learning to use R, a powerful statistical programming language widely used for data analysis, visualisation, statistical modelling, and reproducible reporting.

Through confidence intervals and hypothesis tests, you will see how statistics supports evidence-based decision-making. Regression modelling will help you explain and predict relationships in data, while methods such as analysis of variance and principal component analysis introduce further ways to explore complex datasets.

By combining statistical theory with practical data analysis, this module prepares you for more advanced study and for careers where evidence, modelling, and data-driven decisions matter.

What you will learn

In this module, you will:

• explore, summarise, and visualise data using R
• interpret patterns, relationships, and uncertainty in data
• study sampling, estimation, and confidence intervals
• carry out hypothesis tests and goodness-of-fit tests
• analyse relationships, compare groups, reduce complex datasets, and build regression models
• create reproducible reports using statistical software.

By the end of this module, you will be able to:

• use R to analyse, visualise, and model data
• make statistical inferences from sample data
• construct and interpret confidence intervals
• test whether different populations are independent based on sample data
• apply hypothesis tests in a range of situations
• build and assess simple and multiple regression models
• communicate statistical results clearly and reliably

Assignments / assessments

• Coursework (40%)
• Exam (60%)

Teaching methods / timetable

• two one-hour lectures each week, introducing the main statistical ideas, methods, and worked examples
• lecture notes available before class, helping you prepare and focus on understanding during sessions
• interactive class discussion, giving you opportunities to ask questions and connect ideas
• two one-hour tutorials each week, where you will practise solving problems individually and in groups
• support from lecturers and peers, helping you work through difficulties and build confidence with statistical methods