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Categorical Data and Generalised Linear Models - BCA809

The aim of this unit is to enable students to use generalized linear models (GLMs) and other methods to analyse categorical data with proper attention to the underlying assumptions. There is an emphasis on the practical interpretation and communication of results to colleagues and clients who may not be statisticians. Unit contents: 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; generalized 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.

Because of the multi-institutional nature of the BCA units, there is an early cut-off for enrolment in this unit. These dates are:
Session 1: 19 February 2018
Session 2: 23 July 2018

Credit Points: 4
When Offered:

S2 External - Session 2, External (On-campus sessions: None)

Staff Contact(s): Professor Gillian Heller

(BCA801 and BCA805) or STAT806 or STAT810 Prerequisite Information


(Admission to MBioStat or PGDipBioStat or PGCertBioStat or GradDipBioStat or GradCertBioStat and BCA808) or (admission to MAppStat or GradDipAppStat or MSc or MActPrac)

NCCW(s): STAT811
Unit Designation(s):


Assessed As: Graded
Offered By:

Department of Statistics

Faculty of Science and Engineering

Course structures, including unit offerings, are subject to change.
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