Advanced Regression Modelling - MA882

Location Term Level Credits (ECTS) Current Convenor 2019-20
Canterbury
(version 2)
Autumn
View Timetable
7 15 (7.5) DR C Villa

Pre-requisites

None

Restrictions

None

2019-20

Overview

Linear model. Least squares. General linear model; simple and multiple regression, polynomial regression. Model selection, residuals, outliers, diagnostics. Analysis of variance. Generalised linear model.

Discrete data analysis. Review of Binomial, Poisson, negative binomial and multinomial distributions. Properties, estimation, hypothesis tests.

Contingency tables. Tests for independence. Measures of association. Logistic models.

Multidimensional tables. Log–linear models; fitting and model selection.

Details

This module appears in:


Contact hours

30 hours

Method of assessment

80% examination and 20% coursework

Indicative reading

Draper, N. R., and Smith, H. (1998). Applied Regression Analysis, 3rd ed. New York, Wiley.
McCullagh, P., and Nelder, J. A. (1989). Generalized Linear Models, 2nd ed. London, Chapman and Hall.
Everitt, B.S. (1992). The Analysis of Contingency Tables. London, Chapman and Hall.

See the library reading list for this module (Canterbury)

Learning outcomes

The intended subject specific learning outcomes. On successfully completing the module students will be able to:

1 demonstrate a systematic understanding of regression analysis and analysis of variance, and be able to apply these techniques critically to real world data using statistical packages;
2 interpret the results of analysis, and communicate these clearly and concisely to other statisticians and to non-statisticians;
3 demonstrate an appreciation of the limitations of standard regression and analysis of variance models for discrete data, and a clear understanding of how these models can be generalised so as to be more appropriate for discrete data.

The intended generic learning outcomes. On successfully completing the module students will be able to:

1 apply a logical, mathematical approach to their work;
2 appropriately manipulate data for regression analysis;
3 demonstrate an appreciation of the need for techniques used to be appropriate to the type of data available.

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