The aim of this module is to equip the student with statistical techniques to evaluate evidence, to design experiments, to explore relationships and to make predictions.
Probability
Definitions of probability; laws of probability; the independence assumption. dependent events; conditional probability and Bayes’ Theorem.
Experimental Design and ANOVA
Principles of experimental design: randomisation, replication, blocking. Hypotheses, models, and assumptions. Interpreting ANOVA tables and interaction plots. Application to measurement systems.
Categorical Data Analysis
Contingency table analysis; sampling techniques; defining the categories.
Regression Analysis
Predictor and response; correlation; regression; assumptions; prediction; prediction intervals and confidence intervals; hypothesis testing in regression; curvilinear and multiple regression; selection of variables.
| Module Content & Assessment | |
|---|---|
| Assessment Breakdown | % |
| Other Assessment(s) | 30 |
| Formal Examination | 70 |