Statistical Modelling III
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Description
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.
Objective
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 nonlinear 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.
Content
Topics covered are: the linear model, least squares estimation, generalised least squares estimation, properties of estimators, the GaussMarkov 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, BoxCox models, model selection and model building strategies; models with complex error structure, splitplot experiments; logistic regression models.
 
Year  Semester  Level  Units 

2013  1  3  3 
Graduate attributes
Linkage future
This course is not recorded as prequisite for other courses.
Recommended text
None.
