This module is being introduced to replace MS5432 and is a title change only to reflect Programme Modifications approved in the 2016/17 academic year The purpose of the module is help students gain an detailed understanding of the key features of experimental design from theory, through construction, to application. To use an appropriate software package (eg. SAS JMP or Minitab) to design an experiment, analyse and interpret the resulting data To plac emphasis is on the understanding, interpretation and relevance of statistical analysis rather than on calculations To ensure students will have sufficient statistical knowledge for the Six Sigma Master Black Belt level.

Measurement Systems Analysis. Analysis of Residuals. Blocking in two level designs. Fold-over designs. repeat experiments vs. replicate experiments. Building the regression model and verification of the model. Analysis of single replicate designs. Data transformations in a factorial design. Analysis of multiple response experiments e.g. mean response and variability of response. Addition of centre points to a two level design. Response Surface Methods: Method of Steepest Ascent, verifying adequacy of first order model. Analysis of second order response surface. Characterising the response surface. Selecting designs for fitting response surfaces. Central composite designs. Box-Behnken designs. Blocking in Response surface designs. Evolutionary Operation. Experiments with Random factors: The random effects model. Rules for Expected means squares. Estimation of variance components. Nested Designs. Categorical data analysis. Probit model, Logistic and Poisson regression. Introduction to Robust Statistics eg median polish and robust Taguchi methods. Solution of static and dynamic problems using Taguchi methods.

Upon completion of this module the student should: 1. Possess a detailed understanding of experimental design from theory, through construction, to application. 2. Be able to use an appropriate software package (e.g. Minitab) to run an experiment 3. To understand the interpretation and reliance of results from statistical analysis

Be able to explain the rationale behind process investigation studies and the appropriateness of Design of Experiments

Develop skills in the practical application of process studies and Design of Experiments.

Delivery will be through a blended learning approach. This will include face-to-face classroom workshops, webinars and moderated discussion boards. Material will be introduced through expert workshops and learning will be developed through prescribed readings and other activities such as case studies, simulations, online activities, computational problems and other media. A series of tutor facilitated skills based workshops at which practical exercises and applications reinforce the learning material studied by participants in the on-line environment. This mode guides participants through material but requires them to deploy the learning in their own organisation. Assessment of students will be based on a combination of regular assignments submitted throughout the course in conjunction with delivery of a company critical design project. 1) Be knowledgeable of the requirements for structuring studies for the evaluation of process understanding 2) Be creative in structuring statistical studies for robust analysis 3) Understand the importance of correctly designing studies to support the investigation 4) Be articulate in presenting and describing the results of the statistical studies 5) Understand improvement roadmaps for achieving goals 6) Be appreciative of the impact on variation on process behaviour