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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2013/2014 -
- ARCHIVE as at 1 September 2013 for reference only
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DRPS : Course Catalogue : School of Mathematics : Mathematics

Undergraduate Course: Multivariate Data Analysis (MATH10064)

Course Outline
SchoolSchool of Mathematics CollegeCollege of Science and Engineering
Course typeStandard AvailabilityAvailable to all students
Credit level (Normal year taken)SCQF Level 10 (Year 4 Undergraduate) Credits10
Home subject areaMathematics Other subject areaNone
Course website None Taught in Gaelic?No
Course descriptionOptional course for the Honours Degrees in Mathematics & Statistics and Economics & Statistics and MSc in Statistics and OR.
Syllabus summary:

- Estimation and Hypothesis Testing for multivariate normal data;
- Principal Component Analysis and Factor Analysis;
- Discriminant Analysis;
- Cluster Analysis,
- Correspondence Analysis.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed: Linear Statistical Modelling (MATH10005) AND Likelihood (MATH10004)
Co-requisites
Prohibited Combinations Other requirements None
Additional Costs None
Information for Visiting Students
Pre-requisitesNone
Displayed in Visiting Students Prospectus?No
Course Delivery Information
Delivery period: 2013/14 Semester 2, Available to all students (SV1) Learn enabled:  Yes Quota:  None
Web Timetable Web Timetable
Course Start Date 14/01/2014
Breakdown of Learning and Teaching activities (Further Info) Total Hours: 100 ( Lecture Hours 22, Seminar/Tutorial Hours 5, Summative Assessment Hours 2, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 69 )
Additional Notes
Breakdown of Assessment Methods (Further Info) Written Exam 95 %, Coursework 5 %, Practical Exam 0 %
Exam Information
Exam Diet Paper Name Hours:Minutes
Main Exam Diet S2 (April/May)MATH10064 Multivariate Data Analysis2:00
Summary of Intended Learning Outcomes
Understanding of underlying theory for the analysis of multivariate data.
Ability to
1. choose appropriate procedures for multivariate analysis
2. use the R language to carry out analyses
3. interpret the output of such analyses
Assessment Information
See 'Breakdown of Assessment Methods' and 'Additional Notes', above.
Special Arrangements
None
Additional Information
Academic description Not entered
Syllabus Multivariate normal distribution; maximum likelihood estimation, Wishart's distribution, Hotelling's T2 and hypothesis testing for multivariate normal data.

Principal Components Analysis and derivation of principal components; PCA structural model; PCA on normal populations; biplots; Factor Analysis orthogonal factor model; estimation and factor rotation.

Linear discriminant analysis; Fisher┐s method, discrimination with two groups; discrimination with several groups.

Hierarchical clustering methods, measures of distance, non-hierarchical methods, model-based clustering.

Concepts of correspondence analysis, chi-square distance and inertia, multiple correspondence analysis
Transferable skills Not entered
Reading list Johnson, R.A., Wichern, D.W., 2007. Applied Multivariate Statistical Analysis (6th edition), Pearson Prentice Hall.

Manly, B.F.J, 2005. Multivariate Statistical Methods: A Primer (3rd edition), Chapman & Hall/CRC.

Everitt, B.S., Dunn, G., 2010. Applied Multivariate Data Analysis (2nd edition), Wiley.

Everitt, B.S., Hothorn, T., 2011. An introduction to Applied Multivariate Analysis with R, Springer.
Study Abroad Not entered
Study Pattern Not entered
KeywordsNot entered
Contacts
Course organiser Course secretaryMrs Alison Fairgrieve
Tel: (0131 6)50 5045
Email: Alison.Fairgrieve@ed.ac.uk
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