Categorical Data and GLMs (BSTA5008)


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.

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Further unit of study information


8-12 hours total study time per week, distance learning


3x written assignments (35%, 35%, 30%)


Notes supplied

Faculty/department permission required?


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Study this unit outside a degree

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If you wish to undertake one or more units of study (subjects) for your own interest but not towards a degree, you may enrol in single units as a non-award student.

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If you are from another Australian tertiary institution you may be permitted to underake cross-institutional study in one or more units of study at the University of Sydney.