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January 2019

Statistical Modelling III

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One of the key requirements of an applied statistician is the ability to formulate appropriate statistical models and then apply them to data in order to answer the questions of interest. Most often, such models can be seen as relating a response variable to one or more explanatory variables. For example, in a medical experiment we may seek to evaluate a new treatment by relating patient outcome to treatment received while allowing for background variables such as age, sex and disease severity. In this course, a rigorous discussion of the linear model is given and various extensions are developed. There is a strong practical emphasis and the statistical package R is used extensively.


This course aims to provide students with further fundamental work on modelling with statistics. This is centred around the linear model with generalisations, together with discussion of other models, such as non-linear regression. This course also aims to give students the experience of analysing data, (most of which has arisen from consultancy to industry or research workers in other disciplines), and of writing reports that answer clients' questions.


Topics covered are: the linear model, least squares estimation, generalised least squares estimation, properties of estimators, the Gauss-Markov theorem; geometry of least squares, subspace formulation of linear models, orthogonal projections; regression models, factorial experiments, analysis of covariance and model formulae; regression diagnostics, residuals, influence diagnostics, transformations, Box-Cox models, model selection and model building strategies; models with complex error structure, split-plot experiments; logistic regression models.

Simon Tuke
Lecturer for this course

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This course is not recorded as prequisite for other courses.

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