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| Module Availability |
| Semester 2 |
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| Assessment Pattern |
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Unit(s) of Assessment
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Weighting Towards Module Mark( %)
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2 hour unseen examination
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75
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Two pieces of coursework and class test
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25
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Qualifying Condition(s)
A weighted aggregate mark of 40% is required to pass the module.
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| Module Overview |
| This module introduces least squares fitting, methods of inference based on normal theory, diagnostics and analysis of data from simple designs. |
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| Prerequisites/Co-requisites |
| MAT1025 Probability and Experiment |
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| Module Aims |
| The aims of the module are to introduce concepts involved in general linear models and to equip students with the diagnostic techniques necessary to assess the suitability of a given model. The methods used in analysing simple one-way and two-way experiments and Latin squares designs are also covered. |
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| Learning Outcomes |
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At e At the end of the module a student should:
· be familiar with the main results and methods of the linear and generalised linear models considered in the module;
· be able to apply these results to analyse appropriate data;
· be able to interpret the results from such analyses.
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| Module Content |
Review of one- and two sample normal-based methods, revision of R. Covariance and correlation. The linear model. Least squares estimation. Simple and multiple regression. Selection of variables.
Completely randomised and randomised block experiments. One- and two-way analysis of variance. Interaction. Contrasts.. General regression approach to analysis, residual analysis and diagnostics. |
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| Methods of Teaching/Learning |
| 3 contact hours per week for 10 weeks. Teaching is by lectures, tutorials and computing labs. Learning takes place through lectures, tutorials, practicals, exercises and background reading. |
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| Selected Texts/Journals |
DC
Montgomery
, Design and Analysis of Experiments, Chapman and Hall, 1993.
WJ Krzanowski, An introduction to statistical modelling,
Arnold
, 1998.
BS Everitt, Statistical Analyses using S-Plus, Chapman and Hall, 1994.
GB Wetherill, Intermediate Statistical Methods, Chapman and Hall, 1998. |
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| Last Updated |
| September 10 |
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