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Curricular information is subject to change
On completion of this module students should be able to:
- Propose an appropriate approach to the analysis of categorical data arising from a wide range of sources.
- Analyse binary data and its extensions to multicategory data, formulate and select an appropriate model and display and interpret the results of the model selected.
- Analyse count data (including multiway contingency tables), formulate and select an appropriate model and display and interpret the results of the model selected.
- In all modelling develop a parsimonious description of the data.
- Prepare a report of the analysis for a non-statistical client.
Introduction to categorical data;
Contingency tables; odds ratios; Fisher's exact test; McNemar's test; Maximum likelihood
Generalized linear models: logistic regression; probit regression ; Newton-Raphson; deviance
Poisson regression models
Multicategory logit models
Log linear models for contingency tables
Poisson regression for rates
Student Effort Type | Hours |
---|---|
Lectures | 24 |
Tutorial | 2 |
Computer Aided Lab | 9 |
Specified Learning Activities | 36 |
Autonomous Student Learning | 60 |
Total | 131 |
Knowledge of linear models to the level of STAT30240 is desirable.
Description | Timing | Component Scale | % of Final Grade | ||
---|---|---|---|---|---|
Examination: End of trimester exam | 2 hour End of Trimester Exam | No | Standard conversion grade scale 40% | No | 70 |
Continuous Assessment: There will be approximately 10 assignments some based on computer laboratories | Throughout the Trimester | n/a | Standard conversion grade scale 40% | No | 30 |
Resit In | Terminal Exam |
---|---|
Summer | Yes - 2 Hour |
• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
Assignments will be graded and annotated and discussed in tutorials.
Name | Role |
---|---|
Dr Fabian Ofurum | Tutor |