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 | 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. |
| 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 | None |
| High Demand Course? |
Yes |
Course Delivery Information
|
| Academic year 2023/24, Available to all students (SV1)
|
Quota: 180 |
| 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
85 %,
Coursework
15 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
Coursework 15%, Examination 85% |
| Feedback |
Not entered |
| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
|
| Main Exam Diet S2 (April/May) | MATH10064 Multivariate Data Analysis | 2:00 | |
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.
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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 Victor Elvira Arregui
Tel:
Email: |
Course secretary | Miss Greta Mazelyte
Tel:
Email: |
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