To provide the student with statistical tools for designing experiments, evaluating processes and predicting responses. This module will also provide the student with quality tools for supporting quality functions within a manufacturing organisation.
Single-factor Experiments
Principles of statistical experimental design: randomisation, replication, blocking. Hypotheses, models and assumptions. One-way Analysis of Variance, by hand and using software.
Two-factor experiments
Main effects and interaction effects. Statistical design and analysis of two-factor experiments. Interpreting ANOVA tables and interaction plots.
Multi-factor experiments
Fractional factorial experiments. Curvature. Aliasing. Effects plots. Crossed and nested designs. Fixed and random factors. Statistical power and sample size.
Regression and Association
Prediction intervals and confidence intervals in regression, with application in reliability or elsewhere.Hypotheses testing in regression. Curvilinear and multiple regression. Selection of variables. Categorical data analysis: contingency tables.
Process Capability and SPC
Process capability analysis: statistics for assessing centrality, normality, stability, capability and performance. Process control: construction of SPC charts for variables and attributes. Corrective, preventive and remedial action.
| Module Content & Assessment | |
|---|---|
| Assessment Breakdown | % |
| Other Assessment(s) | 30 |
| Formal Examination | 70 |