Module Information

Module Identifier
Module Title
Regression and Anova
Academic Year
Semester 2
Mutually Exclusive
Other Staff

Course Delivery

Delivery Type Delivery length / details
Practical 11 x 2 Hour Practicals
Lecture 33 x 1 Hour Lectures


Assessment Type Assessment length / details Proportion
Semester Assessment Course work  30%
Semester Exam 2 Hours   (written examination)  70%
Supplementary Exam 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.


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


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.


This module is at CQFW Level 6