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Module Title
LI Multivariate Statistical Analysis
School
Mathematics
Department
Mathematics
Module Code
06 31299
Module Lead
Yin Jing
Level
Intermediate Level
Credits
10
Semester
Semester 1
Pre-requisites
Co-requisites
Restrictions
Available only to students on the JI dual degree pro-grammes
Exclusions
Description
The main contents of this module are: multiple regression, discriminant analysis, cluster analysis, principal component analysis, factor analysis, correspondence analysis, canoni-cal correlation analysis and multidimensional scaling meth-ods. Star plot, Chernoff-Flury Faces, Andrews’ Curves, and scatterplot matrices are used to represent multivariate data. Calculation methods for statistics, such as the mean vector, and covariance matrix are discussed. The module introduces matrix algebra, multivariate distributions, and data transformation. The R statistical programming pack-age is used throughout to illustrate the methods.
Students will write a report about how they analysis real data using some of these multivariate statistical methods.
Learning Outcomes
By the end of the module students should be able to:
Perform exploratory data analysis using graphical and statistical methods.
Understand multivariate statistical models.
Understand the effect of model assumptions in applications.
Appreciate algorithmic differences among similar models.
Write R programs for multivariate models, and analyse the results.
Use multivariate statistical methods and software for real data modelling and analysis.
Assessment
Assessment Methods & Exceptions
Assesment: Assignments (20%)
Paper Report (30%) : a maximum 1500 word report
Final Exam (50%) : a 2 hour examination
Re-assessment (where allowed): a 2-hour resit examination (100%)