MATH5945 is an honours and postgraduate mathematics course. See the course overview below.
Units of credit: 6
Cycle of offering: Term 3
Graduate attributes: The course will enhance your research, inquiry and analytical thinking abilities.
More information: The course outline (PDF) contains information about course objectives, assessment, course materials and the syllabus. The outline will be made available closer to the term of offering.
Important additional information as of 2023
The University requires all students to be aware of its policy on plagiarism.
For courses convened by the School of Mathematics and Statistics no assistance using generative AI software is allowed unless specifically referred to in the individual assessment tasks.
If its use is detected in the no assistance case, it will be regarded as serious academic misconduct and subject to the standard penalties, which may include 00FL, suspension and exclusion.
The online handbook entry contains up-to-date timetabling information.
If you are currently enrolled in MATH5945, you can log into UNSW Moodle for this course.
Data analysts often face a situation where the response outcomes are categories rather than being measured on the interval scale. The accompanying explanatory variables may also be categorical or be continuous. Such type of data is abundant in social sciences, in medical research, particularly in epidemiology and biostatistics, in market research and in other areas. Categorical data are often obtained as counts and presented in the form of contingency tables.
Studying relationships between categorical variables cannot be done using standard regression-type techniques based on the assumption of normality and requires specific methods and techniques.
The core methodology used is the methodology of the generalised linear models. Within this framework, we will study log-linear models, logistic regression, Poisson regression, logit and probit models and analysis of categorised time-to-event data. Specific attention will be paid to the Generalized Likelihood Ratio testing methodology and its application for choosing the "most suitable" model within a hierarchical set of models.
The classical logistic regression models will be extended to cover polytomous responses. The latter are unordered categorical responses which in econometrics are called discrete choices. Computing features prominently in the course and the techniques will be illustrated with the SAS package.