|Delivery Type||Delivery length / details|
|Seminars / Tutorials||2 Hours. (2 x 1 hour example classes)|
|Practical||12 Hours. (6 x 2 hour practical classes)|
|Lecture||14 Hours. (14 x 1 hour lectures)|
|Assessment Type||Assessment length / details||Proportion|
|Semester Assessment||Course work||30%|
|Semester Exam||2 Hours (written examination)||70%|
|Supplementary Assessment||2 Hours (written examination)||100%|
On completion of this module, a student should be able to:
1. explain the rationale behind, and the underlying theory of, the analysis of variance;
2. explain the issues that arise in extending regression from one predictor variable to two;
3. carry out appropriate analyses and draw conclusions.
This module covers the theory of some of the most commonly used statistical techniques - regression and the analysis of variance. It also includes practical application of these important techniques.
This module will provide a thorough grounding in the basic theory associated with some important statistical models.
2. One way classification: the one way ANOVA model. Decomposition of the sum of squares. The ANOVA table and expected mean squares. Distribution of mean squares. The F-test. The treatment effects model. Random effects model. Unbalance design. The idea of blocking. Contrasts.
3. Higher order classification: The two way (balanced) model with replication. Decomposition. Interaction. Further contrasts and multiple comparisons. Expansion to higher order models.
Reading ListSupplementary Text
D D Wackerley, W Mendenhall & R L Scheaffer (2002) Mathematical Statistics with Applications 6th Duxbury. Primo search F Daly, D J Hand, M C Jones, A D Lunn & K J McConway (1995) Elements of Statistics Addison-Wesley Primo search
This module is at CQFW Level 6