Undergraduate Course: Multivariate Data Analysis (MATH10064)
|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
|Summary||Optional course for the Honours Degrees in Mathematics & Statistics and Economics & Statistics and MSc in Statistics and OR.
- Estimation and Hypothesis Testing for multivariate normal data;
- Principal Component Analysis and Factor Analysis;
- Discriminant Analysis;
- Cluster Analysis,
- Correspondence Analysis.
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)
|| Students MUST have passed:
Statistical Methodology (MATH10095)
||Other requirements|| None
Information for Visiting Students
|High Demand Course?
Course Delivery Information
|Academic year 2023/24, Available to all students (SV1)
|Learning and Teaching activities (Further Info)
Lecture Hours 22,
Seminar/Tutorial Hours 5,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
|Assessment (Further Info)
|Additional Information (Assessment)
||Coursework 15%, Examination 85%
||Hours & Minutes
|Main Exam Diet S2 (April/May)||MATH10064 Multivariate Data Analysis||2:00|
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.
|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.
|Graduate Attributes and Skills
|Course organiser||Dr Victor Elvira Arregui
|Course secretary||Miss Greta Mazelyte