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This subject aims to provide students with grounding in advanced linear regression analysis which includes multiple linear regression, Spearman's and Kendall's measures of correlation, principal component analysis and generalised linear models. This subject also focuses on explaining the fundamental concepts of Bayesian statistics, deriving Bayesian estimators, and describing and applying the essential concepts of credibility theory.
Intended learning outcomes
On successful completion of this subject, students should be able to:
- Employ exploratory data analysis techniques to reduce dimensionality of complex data sets and to evaluate the correlation of multivariate data
- Recognise and interpret multiple linear regression models
- Identify the fundamental concepts of a generalised linear model (GLM) and discuss its practical implementation
- Use statistical software to fit regression and generalised linear models to a data set and interpret the results and to write simple functions to complete routine tasks
- Employ the bootstrap method to assess properties of an estimator
- Recognise the fundamental concepts of Bayesian statistics and employ these concepts to derive Bayesian estimators
- Identify and interpret the fundamental concepts of credibility theory
- Apply prerequisite mathematical and statistical concepts to the solution of problems on the above topics
High level of development: written communication; problem solving; statistical reasoning; application of theory to practice; synthesis of data and other information; evaluation of data and other information; use of computer software.
Last updated: 10 December 2019