Area: |
Department of Mathematics and Statistics | ||||||||||||||||||||||||
Credits: |
25.0 | ||||||||||||||||||||||||
Contact Hours: |
4.0 | ||||||||||||||||||||||||
** The tuition pattern below provides details of the types of classes and their duration. This is to be used as a guide only. For more precise information please check your unit outline. ** | |||||||||||||||||||||||||
Lecture: |
3 x 1 Hours Weekly | ||||||||||||||||||||||||
Practical: |
1 x 1 Hours Weekly | ||||||||||||||||||||||||
Prerequisite(s): |
8128 (v.6) Linear Algebra 202 or any previous version AND 8393 (v.8) Statistical Methods 201 or any previous version AND 302315 (v.2) Mathematical Statistics 202 or any previous version |
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Syllabus: |
Introduction to multivariate statistical analysis. Review of matrix algebra. Random vectors, mean vectors, covariance and correlation matrices. Multivariate normal distribution and its properties. Random samples from the multivariate normal and sampling distributions thereof. Inference for multivariate normal parameters, maximum likelihood estimation, generalised linear models (GLM) and inferences for GLMS, one sample and two sample test of hypotheses on the mean vector, comparison of several mean vectors - multivariate analysis ofvariance (MANOVA), discriminant analysis. Analysis of multivariate data using statistical software like R and SAS. | ||||||||||||||||||||||||
** To ensure that the most up-to-date information about unit references, texts and outcomes appears, they will be provided in your unit outline prior to commencement. ** | |||||||||||||||||||||||||
Field of Education: | 010103 Statistics | ||||||||||||||||||||||||
Funding Cluster: | 04 - Mathematics, Statistics | ||||||||||||||||||||||||
SOLT (Online) Definitions*: | Informational *Extent to which this unit or thesis utilises online information | ||||||||||||||||||||||||
Result Type: | Grade/Mark | ||||||||||||||||||||||||
Availability |
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