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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2017/2018

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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
Credit level (Normal year taken)SCQF Level 10 (Year 4 Undergraduate) AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryOptional 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.
Course description 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
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed: ( Linear Statistical Modelling (MATH10005) AND Likelihood (MATH10004)) OR Statistical Methodology (MATH10095)
Co-requisites
Prohibited Combinations Other requirements None
Information for Visiting Students
Pre-requisitesNone
High Demand Course? Yes
Course Delivery Information
Academic year 2017/18, Available to all students (SV1) Quota:  None
Course Start Semester 2
Timetable Timetable
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 )
Assessment (Further Info) Written Exam 95 %, Coursework 5 %, Practical Exam 0 %
Additional Information (Assessment) Coursework 5%, Examination 95%
Feedback Not entered
Exam Information
Exam Diet Paper Name Hours & Minutes
Main Exam Diet S2 (April/May)MATH10064 Multivariate Data Analysis2:00
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
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.
Additional Information
Graduate Attributes and Skills Not entered
KeywordsMVDAn
Contacts
Course organiserDr Javier Palarea-Albaladejo
Tel:
Email: Javier.Palarea@ed.ac.uk
Course secretaryMrs Alison Fairgrieve
Tel: (0131 6)50 5045
Email: Alison.Fairgrieve@ed.ac.uk
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