Introductory description

This module runs partly in term 2 and partly in term 3. It is available for students on a course where it is a listed option and as an Unusual Option to students who have completed the prerequisite modules. It is strongly recommended for any students intending to do substantial data analysis.

Students wishing to pursue the integrated Masters MMORSE are expected to take ST221 in Year 2. Data Science students will find it highly relevant for their third year project. ST221 may form part of the criteria for determining places on ST modules with capped numbers such as ST340 Programming for Data Science and ST344 Professional Practice of Data Analysis.

Pre-requisites for Statistics students: ST115 Introduction to Probability, ST218 Mathematical Statistics A and ST219 Mathematical Statistics B (taken concurrently).
Pre-requisites for Non-Statistics students: ST111/ST112 Probability A & B and ST220 Introduction to Mathematical Statistics. Basic knowledge in R such as covered in ST104 Statistical Laboratory I will be useful.

Results from the coursework from this module may be partly used to determine exemption eligibility in the computer based assessment components of the Institute and Faculty of Actuaries modules CS1, CS2, CM1 and CM2. (Independent application to the IFoA may be required.)

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.

1. Introduction to the R software. Some useful methods of examining large data sets. The use of this package to obtain important summary features in different data structures.
2. A review of the simple linear regression. Distributions of estimators and residuals.
3. An introduction to multiple regression. Estimators of these models. How the study of residuals can inform and refine model choice. How to use R to check the plausibility of such a statistical model and how to use diagnostic plots in combination with the theory of model refinement.
4. Introduction of polynomial regression and various ANOVA models. The coding and interpretation of these models using R.
5. An introduction to linear models for time series and generalized linear models for frequency data.

Learning outcomes

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

• Make use of the language R to explore data sets with appropriate graphs and summary statistics.
• Make use of R to fit appropriate linear models to data sets.
• Understand how various linear models can be proposed, estimated, diagnostically checked, compared and criticised.

Indicative reading list

View reading list on Talis Aspire

Subject specific skills

TBC

Transferable skills

TBC