MA3405 - Multivariate Statistical Methods

Credit points:	03
Year:	2016
Student Contribution Band:	Band 2
Administered by:	College of Science and Engineering

Provide students with practical skills in statistical data mining methods, essential for extracting information from multivariate, highly dimensional, common to the "Big Data" world we live in today. The following topics will be covered: Introduction to multivariate analysis; Cluster analysis; The multivariate normal distribution; Maximum likelihood estimation; Comparison of multivariate means; Principal components; Factor analysis; Discriminant analysis.

Learning Outcomes

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

Prerequisites:

MA2405 OR MA2000

Availabilities
Townsville, Internal, Study Period 2
Census Date 25-Aug-2016
Coord/Lect:	Assoc. Professor Yvette Everingham.
Contact hours:	26 hours lectures 26 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.