Module Identifier MA36510  
Academic Year 2000/2001  
Co-ordinator Mr D A Jones  
Semester Semester 1  
Pre-Requisite MA27010  
Course delivery Lecture   19 x 1hour lectures  
  Seminars / Tutorials   3 x 1hour example classes  
Assessment Exam   2 Hours (written examination)   100%  
  Resit assessment   2 Hours (written examination)   100%  

General description
The Linear Statistical Model encompasses all elementary statistical techniques such as one and two mean procedures, straight line fitting, etc, and much more besides. This module sets such models in a matrix formulation and shows the neatness and breadth of application of such modelling, at the same time illustrating some illuminating applications of matrices.

To introduce the scope and breadth of linear matrix modelling.

Learning outcomes
On completion of this module, a student should be able to:

1. DISTRIBUTION THEORY: Random vectors. Multivariate Normal Distribution. Linear Forms. Quadratic forms. Independence.
2. GENERAL LINEAR MODEL OF FULL RANK: Formulation. Least squares and the normal equations. Properties of their solution. Effect of independent homoscedastic errors. The Gauss-Markov Theorem.
3. INFERENCE IN THE FULL RANK CASE: Confidence statements. Confidence regions. Prediction intervals. Confidence limits for ratios.

Reading Lists
** Supplementary Text
F A Graybill. An Introduction to the General Linear Model. Duxbury
** Essential Reading
R H Myers and J S Milton. A FIrst Course in the Theory of Linear Statistical Models. PWS-Kent