Delivery Type	Delivery length / details
Lecture	14 Hours. (14 x 1 hour lectures)
Seminars / Tutorials	2 Hours. (2 x 1 hour example classes)
Practical	12 Hours. (6 x 2 hour practical classes)

Assessment Type	Assessment length / details	Proportion
Semester Assessment	coursework	30%
Semester Exam	2 Hours (written examination)	70%
Supplementary Assessment	2 Hours (written examination)	100%

Learning Outcomes

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.

Brief description

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.

Aims

This module will provide a thorough grounding in the basic theory associated with some important statistical models.

Content

1. Regression: The regression model, Ordinary least squares and the Normal equations. Detailed analysis of the two regressor model. Residuals and the residual sum of squares. Sequential sum of squares. Standardised residuals. Decomposition of the sum of squares.
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.

Course Delivery

Assessment

Learning Outcomes

Brief description

Aims

Content

Reading List

Notes