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Unit of study_
BSTA5008: Categorical Data and Generalised Linear Model
2022 unit information
The aim of this unit is to enable students to use generalised linear models (GLMs) and other methods to analyse categorical data, with proper attention to underlying assumptions. There is an emphasis on the practical interpretation and communication of results to colleagues and clients who might not be statisticians. This unit covers: Introduction to and revision of conventional methods for contingency tables especially in epidemiology; odds ratios and relative risks, chi-squared tests for independence, Mantel-Haenszel methods for stratified tables, and methods for paired data. The exponential family of distributions; generalised linear models (GLMs), and parameter estimation for GLMs. Inference for GLMs - including the use of score, Wald and deviance statistics for confidence intervals and hypothesis tests, and residuals. Binary variables and logistic regression models - including methods for assessing model adequacy. Nominal and ordinal logistic regression for categorical response variables with more than two categories. Count data, Poisson regression and log-linear models.
Unit details and rules
Managing faculty or University school:
Medicine and Health
| Study level | Postgraduate |
|---|---|
| Academic unit | Public Health |
| Credit points | 6 |
|
Prerequisites:
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BSTA5007 |
|---|---|
|
Corequisites:
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|
None |
| Prohibitions:
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BSTA5210 or BSTA5211 |
| Assumed knowledge:
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None |
At the completion of this unit, you should be able to:
- LO1. Formulate a generalized linear model and derive its maximum likelihood estimators.
- LO2. Answer research questions by exploring data graphically; selecting and applying appropriate modelling techniques; appraising underlying model assumptions and goodness of fit, and modifying the analysis if required
- LO3. Perform model selection and test hypothesis.
- LO4. Apply the generalized additive model to incorporate nonlinear forms of the predictors and use random effects or generalized estimating equations to model correlated data.
- LO5. Use statistical software to create model output and interpret them.
Unit availability
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Important enrolment information
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