Course Catalogue

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This course introduces the theory and application of advanced regression models including non-linear, nonparametric and generalised additive models.

Lectures: 20 lectures (2 hours per week, at times to be arranged)

Tutorials: fortnightly (at times to be arranged)

Practicals: 2, two hour computing sessions (at times to be arranged)

Some optional courses may be constrained by space and entry to these is not guaranteed unless you are in a programme for which this is a compulsory course.

STATS4040 Flexible Regression

Assessment

90-minute, end-of-course examination (85%)

Project (15%)

Reassessments are normally available for all courses, except those which contribute to the Honours classification. For non Honours courses, students are offered reassessment in all or any of the components of assessment if the satisfactory (threshold) grade for the overall course is not achieved at the first attempt. This is normally grade D3 for undergraduate students and grade C3 for postgraduate students. Exceptionally it may not be possible to offer reassessment of some coursework items, in which case the mark achieved at the first attempt will be counted towards the final course grade. Any such exceptions for this course are described below. 

To develop the theory and application of advanced regression modelling by introducing students to non-linear, nonparametric and generalised additive modelling.

To introduce the idea of smoothing in a regression context.

To introduce a variety of approaches for smoothing including local polynomial regression and regression splines.

To explain and illustrate the appropriate uses and restrictions of advanced regression models.

To develop appropriate methods for the construction, selection and evaluation of advanced regression models.

To illustrate to students the application of advanced regression models in a variety of practical contexts.

By the end of this course students will be able to:

■ formulate advanced regression models including non-linear, non-parametric and generalised additive models;

■ state, describe and compare methods for smoothing in a regression context;

■ describe techniques to choose smoothing parameters;

■ state expressions for degrees of freedom of a smoother;

■ apply smoothing in a wide variety of practical regression contexts;

■ describe methods to fit advanced regression models;

■ use hypothesis tests and appropriate criterion for model selection;

■ assess the goodness of fit of an advanced regression model;

■ fit advanced regression models in R;

■ interpret the output of R procedures for advanced regression models.

■ implement advanced regression methods using R and write a clear, concise report summarising their results.

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.