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Curricular information is subject to change
The student will be familiarised with the basic multivariate techniques, why they work, how to use them and where their use is appropriate. The student will develop skills to conduct an analysis of multivariate data using statistical software, interpret the results and draw conclusions. The student will be made aware of the advantages and limitations of each method.
Additionally students will be equipped with the necessary transferable skills for statistical analysis in the real world: developing application domain knowledge and presenting reports of analyses.
Introduction to multivariate data.
Principal components analysis
|Student Effort Type||Hours|
|Computer Aided Lab||
|Specified Learning Activities||
|Autonomous Student Learning||
Basic statistics modules covering e.g. hypothesis testing, inference, regression, maximum likelihood estimation. Elementary matrix algebra including eigenvalues and eigenvectors.
|Description||Timing||Component Scale||% of Final Grade|
|Examination: Examination||2 hour End of Trimester Exam||No||Alternative linear conversion grade scale 40%||No||
|Assignment: Assignments involving use of e.g. statistical software, oral presentations, report writing.||Throughout the Trimester||n/a||Alternative linear conversion grade scale 40%||No||
|Resit In||Terminal Exam|
|Summer||Yes - 2 Hour|
• Group/class feedback, post-assessment
Not yet recorded.
|Ms Sinead Mcparland||Lecturer / Co-Lecturer|
|Mr Ganesh Babu||Tutor|
|Mr Wenxuan Liu||Tutor|