# DEGREE REGULATIONS & PROGRAMMES OF STUDY 2019/2020

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# 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 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
 Pre-requisites Students MUST have passed: ( Linear Statistical Modelling (MATH10005) AND Likelihood (MATH10004)) OR Statistical Methodology (MATH10095) Co-requisites Prohibited Combinations Other requirements None
 Pre-requisites None High Demand Course? Yes
 Academic year 2019/20, 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 95 %, Coursework 5 %, Practical Exam 0 % Additional Information (Assessment) Coursework 5%, Examination 95% Feedback Not entered Exam Information Exam Diet Paper Name Hours & Minutes Main Exam Diet S2 (April/May) Multivariate Data Analysis 3:00
 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 Not entered Keywords MVDAn
 Course organiser Dr Javier Palarea-Albaladejo Tel: Email: Javier.Palarea@ed.ac.uk Course secretary Miss Sarah McDonald Tel: (0131 6)50 5043 Email: sarah.a.mcdonald@ed.ac.uk
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