|Delivery Type||Delivery length / details|
|Lecture||19 Hours. (19 x 1 hour lectures)|
|Seminars / Tutorials||3 Hours. (3 x 1 hour example classes)|
|Assessment Type||Assessment length / details||Proportion|
|Semester Exam||2 Hours (written examination)||100%|
|Supplementary Exam||2 Hours (written examination)||100%|
On completion of this module, a student should be able to:
1. explain and use the reduction in sum of squares principle;
2. formulate and carry out a test of a linear hypothesis;
3. explain the importance of good design matrix structures;
4. compare different models suggested for the same data sets;
5. construct and fit models involving coincident or parallel straight lines as arise in biological and pharmaceutical assays;
6. fit models of less than full rank;
7. explain the idea of estimability;
8. describe the concept of a generalized linear model and, in some appropriate situations, construct and fit suitable models.
This module builds on the work in MA36510 by focusing on some of the many and varied applications of the Linear Model and considers techniques and modifications that have been motivated by them. Modern developments in the area are also considered.
To make the student aware of some of the applications of Linear Models and to consider new developments.
2. COMPARISON OF MODELS: Orthogonality. Orthogonal polynomials. Weighing designs. Brief treatment of design optimality.
3. GENERALIZED LINEAR MODELS: Basic ideas. The exponential family. Link functions and canonical links. Deviance and deviance residuals. Examples including models for exponential, binomial and Poisson data.
Reading ListRecommended Text
R H Myers and J S Milton (1991) A first course in the theory of linear statistical models PWS-Kent Primo search Supplementary Text
F A Graybill (1976) Theory and application of the linear models Duxbury Primo search
This module is at CQFW Level 6