Statistics is the study of uncertainty, which arises in all aspects of life. How long will I live for? What is the probability that Birmingham City will win the FA Cup this season? Will it rain today? What is the probability that I will win the lottery this week? Statistical theory and methods are fundamental to our understanding of such uncertainty, and are an increasingly sought after skill. For example, Google uses statistics to improve their search algorithms; medical research uses statistics to design and analyse clinical trials evaluating whether a new cancer treatment is effective; and actuarial and economic teams use statistics to make accurate predictions about future risks and outcomes. This module presents a parallel development of statistical theory and methods, building on the introductory material in Probability & Statistics. Topics covered include classical linear models, with an introduction to the analysis of variance; basic methods for handling discrete data; further work in the theory of probability distributions; and some aspects of the theory of estimation and hypothesis testing. In the computing sessions, a statistical package is used to illustrate the application of statistical methods to the analysis of some typical data sets.

Learning Outcomes

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

demonstrate a basic understanding of the principles of statistical inference;

be able to handle simple problems in the distributional calculus;

be able to identify standard situations to which statistical models apply;

be able to build statistical models and use them for the analysis of data;

demonstrate fluency with a standard statistical package.

Assessment

25671-07 : Raw Module Mark : Coursework (100%)

Assessment Methods & Exceptions

2 hour Written Unseen Summer Examination (80%); In-course Assessment (20%). Assessed as a single 20-credit module rather than a combined paper.