Undergraduate Course: Multivariate Data Analysis (MATH10064)
Course Outline
| School | School of Mathematics |
College | College of Science and Engineering |
| Course type | Standard |
Availability | Available to all students |
| Credit level (Normal year taken) | SCQF Level 10 (Year 4 Undergraduate) |
Credits | 10 |
| Home subject area | Mathematics |
Other subject area | None |
| Course website |
None |
Taught in Gaelic? | No |
| Course description | Optional 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. |
Information for Visiting Students
| Pre-requisites | None |
| 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 Analysis | 2:00 | | |
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 |
| Keywords | Not entered |
Contacts
| Course organiser | |
Course secretary | Mrs Alison Fairgrieve
Tel: (0131 6)50 5045
Email: Alison.Fairgrieve@ed.ac.uk |
|
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|
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