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Multivariate Data Analysis - MAT00094M

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  • Department: Mathematics
  • Module co-ordinator: Dr. Benjamin Thorpe
  • Credit value: 20 credits
  • Credit level: M
  • Academic year of delivery: 2024-25
    • See module specification for other years: 2023-24

Module summary

This module introduces various models and methods for multivariate data analysis as well as data-driven nonparametric models and methods.

Related modules

Pre-requisite modules

Co-requisite modules

  • None

Prohibited combinations


Module will run

Occurrence Teaching period
A Semester 2 2024-25

Module aims

This module introduces various models and methods for multivariate data analysis as well as data-driven nonparametric models and methods.

Module learning outcomes

By the end of the module, students should be able to:

  1. Work with different models for multivariate data and nonparametric models.

  2. Apply the main techniques of multivariate data analysis and nonparametric estimation methods, and pick appropriate techniques to apply to different types of data.

  3. Use the statistical package R to analyse multivariate data

  4. Carry out self-directed analysis of more complex problems requiring more advanced techniques or analysis methods (for M-level students)

Module content

This module can be split into two parts. Part (i) covers seven classic topics in multivariate data analysis to be taught in the first seven weeks; and part (ii) introduces some commonly-used nonparametric models and methods including the kernel and local polynomial estimations to be taught in the last three weeks.

Assessment

Task Length % of module mark
Closed/in-person Exam (Centrally scheduled)
Closed exam : Multivariate Data Analysis		 3 hours 100

Special assessment rules

None

Reassessment

Task Length % of module mark
Closed/in-person Exam (Centrally scheduled)
Closed exam : Multivariate Data Analysis		 3 hours 100

Module feedback

Current Department policy on feedback is available in the student handbook. Coursework and examinations will be marked and returned in accordance with this policy.

Indicative reading

T.W. Anderson. An Introduction to Multivariate Statistical Analysis. New York : Wiley, 2003.

C. Chatfield and A. J. Collins. Introduction to Multivariate Analysis. Chapman and Hall, 1980.

B. Everitt. An R and S-plus Companion to Multivariate Analysis. Springer, 2005.

J. Fan and I, Gijbels. Local Polynomial Modelling and Its Applications. Chapman and Hall/CRC, 1996.

K. V. Mardia, J. T. Kent and J. M. Bibby. Multivariate Analysis. Academic Press, 1979.


The information on this page is indicative of the module that is currently on offer. The University is constantly exploring ways to enhance and improve its degree programmes and therefore reserves the right to make variations to the content and method of delivery of modules, and to discontinue modules, if such action is reasonably considered to be necessary by the University. Where appropriate, the University will notify and consult with affected students in advance about any changes that are required in line with the University's policy on the Approval of Modifications to Existing Taught Programmes of Study.