Statistical Modelling and Data Analysis

• Credit value: 15 credits at Level 6
• Module coordinator: To be confirmed
• Assessment: Coursework (20%) and an exam (80%)

This module will be beneficial to you if you are interested in developing your understanding of multivariate data analysis and regression modelling.

Indicative module syllabus

Part A: Multivariate Analysis

• Covariance and Correlation Matrices
• Principal Components Analysis
• Procedures based on The Multivariate normal distribution
• Discriminant Analysis

Part B: Generalized Linear Modelling

• Use of log-linear models for the analysis of contingency tables in two or more dimensions
• Modelling Binary Response Data
• Numerical procedures for parameter estimation

• to provide a basic introduction to the concepts and techniques of multivariate data analysis
• to extend knowledge in the area of regression modelling from multiple linear regression to generalized linear modelling
• to provide a working knowledge of how basic multivariate analysis, and some types of generalised linear modelling, can be implemented in a high level statistical package such as S-PLUS and applied to realistic data sets.

• N. H. Bingham and John M. Fry, Regression: Linear Models in Statistics, Springer, 2010.
• Wojtek J.Krzanowski, An Introduction to Statistical Modelling, Arnold, 1998.
• Wojtek J. Krzanowski, Principles of Multivariate Analysis: A User's Perspective (Revised Edition), Oxford University Press, 2000.