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, nonhierarchical methods, modelbased clustering.
Concepts of correspondence analysis, chisquare distance and inertia, multiple correspondence analysis

Entry Requirements (not applicable to Visiting Students)
Prerequisites 
Students MUST have passed:
Statistical Methodology (MATH10095)

Corequisites  
Prohibited Combinations  
Other requirements  None 
Information for Visiting Students
Prerequisites  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.

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: victor.elvira@ed.ac.uk 
Course secretary  Miss Greta Mazelyte
Tel:
Email: greta.mazelyte@ed.ac.uk 

