620-372 Applied Statistical Analysis

Passing 620-372 precludes subsequent credit for 620-270.

620-371 and one of 620-202 or [01]620-204.

This subject extends the theory of inference developed in 620-202 Statistics and demonstrates how it is applied in practice. In addition, a number of recently developed techniques for analysing data, which involve extensive computer computations, are considered. Students will develop an understanding of the principles of statistical inference and will learn to use a number of important specific techniques in applied statistics.

Topics covered include principles and fundamental results in estimation and hypothesis testing; including consistency, sufficiency, minimum variance unbiased estimation, likelihood methods and associated asymptotic theory, optimal tests and likelihood ratio tests. Generalised linear models. Application of the above methodologies - in particular, application to logistic regression and log-linear models. Introduction to Bayesian methods.

Re-sampling methods; jack-knife and the bootstrap; use of the bootstrap for exploring the sampling distribution of an estimator.

The subject develops the students' generic skills, including thinking critically and organising knowledge; analysing data, interpreting results and presenting conclusions in a clear and comprehensible manner; using computers for data analysis and presentation; and solving problems.

Up to 50 pages of written assignments and a 3-hour end-of-semester written examination.