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Module Descriptor School of Computer Science and Statistics

Module CodeST3011
Module NameMLA
Module Short TitleMultivariate Linear Analysis (MLA)
Semester Taught1
Contact Hours

Lecture hours: 22, Lab hours: 11, Tutorial hours: 0 Total hours:33 

Module PersonnelDr. Brett Houlding   
Learning Outcomes

When students have successfully completed Multivariate Analysis they should be able to:  

  • Define and describe various classical dimension reduction techniques for multivariate data.
  • Implement clustering and/or classification algorithms and assess and compare the results.
  • Interpret output of data analysis performed by a computer statistics package.    
Learning Aims

Classical multivariate techniques of discriminant analysis, principal component analysis, clustering and logistic regression are examined. There is a strong emphasis on the use and interpretation of these techniques.  More modern techniques, some of which address the same issues, are covered in the SS module Data Mining.   

Module Content
  • Multivariate Analaysis      
  • Principal Components Analysis
  • Multidimensional Scaling
  • Factor Analysis
  • Hierarchical and Iterative Clustering
  • K-Nearest Neighbours
  • Discriminant Analysis
  • Logistic Regression  
Recommended Reading List

Multivariate Analysis:   Introduction to Multivariate Analysis, C. Chatfield and A. Collins, Chapman & Hall  

Module Prerequisites
Assessment Details

Exam: 80%     (2 hours)Coursework: 20%. 

Module Website
Academic Year of Data2018/19