Generalized Linear Models

Course code STAT3030Units 10Level 3000Faculty of Science and Information TechnologySchool of Mathematical and Physical Sciences

How do we model data of very different types in a consistent way? This course explores generalized linear models and illustrates how methods for analysing continuous and categorical data fit into this framework.

Available in 2015

Callaghan CampusSemester 1
Previously offered in 2014
ObjectivesOn successful completion of this course, students will be able:

1.To help students understand and use the principles of statistical modelling;
2.To provide a unified conceptual and theoretical framework for many of the most commonly used statistical methods including multiple linear regression, analysis of variance and logistic regression;
3.To develop skill in statistical computing.
ContentTopics include:
. Linear models
. Model fitting as an approach to statistical analysis
. Exponential family of distributions
. Maximum likelihood estimation
. Inference methods based on model fitting
. Generalised Linear Models
. Models for continuous data (regression analysis of variance)
. Models for categorical data (logistic regression for nominal and ordinal data, Poisson regression and log-linear models)
. Generalised Additive models
Replacing Course(s)n/a
Transitionn/a
Industrial Experience0
Assumed KnowledgeSTAT2010 Fundamentals of Statistics
STAT2000 Applied statistics and Research Methods
Modes of DeliveryInternal Mode
Teaching MethodsLecture
Computer Lab
Assessment Items
Essays / Written Assignments
Examination: Formal
Contact HoursLecture: for 2 hour(s) per Week for Full Term
Computer Lab: for 2 hour(s) per Week for Full Term
Timetables2015 Course Timetables for STAT3030