620-372 Applied Statistical Inference

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. 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; and generalised linear models. Application of the above methodologies to logistic regression (analysis of grouped and ungrouped binary data), log-linear models (analysis of two- and higher-dimensional contingency tables) and survival analysis (Kaplan-Meier estimates, parametric models, non-parametric models) is also studied.

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 during semester (20%) and a 3-hour end-of-semester written examination (80%).