Categorical Data Analysis and Generalised Linear Models
This course presents methods for analyses of categorical data, with a key focus on generalised linear models (GLMs). Students will study contingency tables, and gain experience with a range of generalised linear models appropriate to binary, count, nominal, ordinal and time-to-event response variables. There will be an emphasis on understanding the theoretical basis for the models, gaining practical experience with model fitting, checking model assumptions and interpreting results for non-statistical colleagues.
|Faculty||Faculty of Health and Medicine|
|School||School of Medicine and Public Health|
Semester 2 - 2017
Semester 2 - 2018 (Online)
On successful completion of this course, students will be able to:
Topics covered include contingency tables, the exponential family and generalised linear models, estimation and modelling using logistic regression, log-linear models, Poisson regression, logit and probit models, multinomial models and Cox regression.There will be an emphasis on fitting appropriate models using SAS statistical software, checking model fit and interpreting results.
per Week for
|Timetable||2017 Course Timetables for BIOS6940|