Course Description: This course is intended to introduce students to generalised linear modelling methods, with emphasis on, but not limited to, common methods for analyzing categorical data.  Topics covered include a review of multiple linear regression and the analysis of variance, log-linear models for contingency tables, logistic regression for binary response data, Poisson regression, model selection and model checking, mixed effects models.  Additional topics may include Bayesian analysis for generalized linear models and generalized mixed effect models.

Learning Outcomes:

1. Understand the role of generalised linear modelling techniques (GLMs) in modern applied statistics and implement methodology.
2. Understand the underlying assumptions for GLMs and perform diagnostic checks; appreciate potential problems.
3. Perform statistical analysis using statistical software in addition to learning the underlying theory and methodologies.
4.  Achieve a higher working-level understanding of the statistical techniques used (beyond ideas and processes involved).

• Mid semester exam (1 hour) 20%
• Final exam (3 hours) 80%

10 hours per week

At least an average of 65% (or equivalent) in the final two years of an Australian undergraduate degree with at least two years of university level statistical and mathematical study including calculus and linear algebra, as well as mathematical statistics and linear regression theory.

STAT2008/STAT6038 or enrolment in the Master of Applied Statistics

See Course Website:  http://ecocomm.anu.edu.au/courses/course.asp?code=STAT7030

Please refer to Course Website