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MA3405 - Multivariate Statistical Methods

Credit points: 3
Year: 2014
Student Contribution Band: Band 2
Administered by: School of Engineering (pre 2015)

Multivariate descriptive statistics, linear models and MANOVA, principal components, correspondence, factor, discriminant, canonical correlation and cluster analysis. Use of packaged statistics software.

Learning Outcomes

  • to give the student a firm understanding of statistical methods, essential for extracting information from multivariate and "real world" data.
Prerequisites: MA2405

Availabilities

Townsville, Internal, Study Period 2
Census Date 28-Aug-2014
Coord/Lect: Professor Yvette Everingham.
Workload expectations:
  • 26 hours lectures
  • 13 hours practicals
Assessment: end of semester exam (70%); other exams (10%); assignments (20%).

Note: Minor variations might occur due to the continuous Subject quality improvement process, and in case of minor variation(s) in assessment details, the Subject Outline represents the latest official information.