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.
