|Module Title||LINEAR STATISTICAL MODELS|
|Co-ordinator||Mr D A Jones|
|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%|
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.
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.
** 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