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MATH5855 is a Post Graduate Mathematics course also available as an Honours Course. See the course overview below.

Units of credit: 6

Pre-requisites:  UNSW UG students - MATH2801, MATH2901or admitted to the postgraduate programs of the School of Mathematics and Statistics and as an elective in some approved programs.

Cycle of offering: Term 3

Graduate attributes: The course will enhance your research, inquiry and analytical thinking abilities.

More information:  The Course outline will be made available closer to the start of term - please visit this website: www.unsw.edu.au/course-outlines

Important additional information as of 2023

UNSW Plagiarism Policy

The University requires all students to be aware of its .

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.

°Õ³ó±ðÌý contains information about the course. The timetable is only up-to-date if the course is being offered this year.

If you are currently enrolled in MATH5855, you can log into  for this course.

Multivariate statistical analysis is performed with the aim to encompass the data concerning all variables into one analysis. This allows for a better and deeper investigation of the relationships between the variables in comparison to the piecemeal analyses of portions of the data.

It also requires more advanced mathematical and computational techniques in comparison to the univariate analysis but the effort pays off in many ways in the quality of the resulting statistical analysis. Most multivariate methods are easier to be described and discussed under the assumption of multivariate normal distribution of the data and this will be the starting point of the course. We shall discuss likelihood ratio tests for multivariate means, for covariance matrices and covariance structures.

Estimation and testing aspects of correlations, partial correlations, and multiple correlations will be studied then. Important practical applications such as discriminant analysis, cluster analysis, principal components, canonical analysis, factor analysis and latent variables will be discussed in detail. SAS-based Computing features prominently in the course.