Reference no: EM132725190

STAT 3336C Applied Multivariate Statistics - University Of Technology, Mauritius

ASSIGNMENT

The assignment, in the form of a report, shall include the following:

1. A cover page with the module name, module code, student name, student ID, cohort identification (for example BM/18A/FT), and to state whether you are a re-sit student for the module.
2. Section One of the report must introduce and provide a brief history of the module. The section must provide as well as a literature review, with the appropriate referencing, and at least three real-life applications of the module. You must use the Harvard referencing style.
3. Section Two must provide explanation on any six topics/methods of the module with full mathematical derivations. You must list as well all the basic theories used in the explanation. You should provide at least two full workout examples/applications for each of the chosen topic/method. You may use any programming software, like Matlab, R, etc., for numerical implementation. The illustrated examples/applications should be different from those provided during the blended learning approach.
4. Section Three must include a one page A4 poster for each of the above chosen topic/method from the module. The purpose of the poster is to share your knowledge about the topic/method to any layperson. The poster must include an introduction of the topic/method with a real-life application, related brief derivation and illustration with an example/application.

Assignment

I. Summary ot Module Content:
• Plotting and display of multivarlate data
• Multivariate distributions
• Wishart and Hotelling's distributions
• Maximum likelihood estimators
• Multivariate hypothesis testing
• Multivariate regression analysis
• Clustering methods and result interpretation

Module Aim

This module provides a framework of knowledge that allows students to develop a sound understanding of the fundamental concepts, building blocks, and methodologies of multivariate data analysis.
K. Learning Outcomes
After successful completion of this module, the students should be able to
• Explain what multivariate analysis is and when its application is appropriate
• Define and discuss the specific techniques included in multivariate analysis
• Determine which multivariate technique is appropriate for a specific research problem
• Discuss the nature of measurement scales and their relationship to multivariate techniques
• Describe the conceptual and stadstical Issues inherent in multivariate analyses