Undergraduate Course: Multivariate Data Analysis (MATH10064)
Course Outline
| School | School of Mathematics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 10 (Year 4 Undergraduate) |
Availability | Available to all students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| 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
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
Students MUST have passed:
Statistical Methodology (MATH10095)
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Information for Visiting Students
| Pre-requisites | This is a Year 4, Honours level course. Visiting students are expected to have an academic profile equivalent to the first three years of the BSc (Hons) Mathematics programme (UTMATHB). Students should have passed courses equivalent to Statistical Methodology (MATH10095). |
| High Demand Course? |
Yes |
Course Delivery Information
|
| Academic year 2025/26, 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
80 %,
Coursework
20 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
Coursework 20%, Examination 80% |
| Feedback |
Not entered |
| Exam Information |
| Exam Diet |
Paper Name |
Minutes |
|
| Main Exam Diet S2 (April/May) | MATH10064: Multivariate Data Analysis | 120 | |
Learning Outcomes
On completion of this course, the student will be able to:
- Understand underlying theory for the analysis of multivariate data.
- Choose appropriate procedures for multivariate analysis.
- Use the R language to carry out analyses.
- 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 |
| Keywords | MVDAn |
Contacts
| Course organiser | Dr Natalia Bochkina
Tel: 0131 650 8597
Email: n.bochkina@ed.ac.uk |
Course secretary | Miss Kirstie Paterson
Tel:
Email: Kirstie.Paterson@ed.ac.uk |
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