- To introduce the concept of a time series, and discuss a range of descriptive methods for identifying features of interest
- To present a range of approaches for representing trends and seasonality in a time series, and to assess their relative merits
- To describe the theoretical properties of commonly used time series models
- To describe a range of approaches for predicting future values of a time series
- To show how to apply the techniques from the course to real time series data sets in the statistical package R
- to provide an appreciation of the application of statistical methods and concepts to problems in medicine, especially in clinical trials, epidemiological studies, reliability and validity of measurement, and to discuss the principal ethical issues that arise
- to present the fundamental principles of likelihood-based inference, with emphasis on the large sample results that are widely used in practice
This course aims to develop elements of probability for vectors of random variables; to introduce, and establish important properties of, the multivariate normal (MVN) distribution; to prove rigorously the distributional results which underpin the statistical analysis of the normal linear model; and to introduce multivariate sampling distributions and explain their role in multivariate inference.