Design of Experiments with Minitab

Course details

Many experimenters are using an OFAT (one-factor-at-a-time) approach to their experimental designs. In addition to the issue of inefficiency, this approach fails to identify often crucially important interaction effects among factors. There are available to experimenters advanced analytical tools based on mathematical techniques and utilising special computer software, which will enable them to gain a deep understanding of their processes, including the impact of interactions among factors, and to do so in the most efficient manner with minimum numbers of experimental runs. These modern DOE tools will be presented on this training course.

Learning Outcomes

On successful completion of this training course, delegates should be able to:

• Plan designed experiments to include appropriate factors and responses
• Analyse factor effects and interaction effects using specialist computer software
• Interpret the outcome of designed experiments so as to choose factor settings for optimum process performance
• Demonstrate knowledge of the statistics underlying the design of experiments

Entry Requirements

A prior knowledge of basic statistics is recommended. However, the course will commence with a review of basic statistics, which will be sufficient to provide understanding of the statistical material that will be met during the course. Participants should have knowledge of mathematical principles, for example, Leaving Certificate mathematics.

Course Programme

Day 1

• Introduction to Statistics Underlying Experimental Design
• Mean, variance, standard deviation, degrees of freedom
• The normal, Student-t and F distributions
• Normal probability plots
• Hypothesis testing

Day 2

• DOE Terminology
• Definition of terms such as independent and dependent variables, factors and levels, response, treatment, error and replication
• Planning and Organizing Experiments
• Applying the basic elements of experiment planning and organizing, including determining the experiment objective; selecting factors, responses, and measurement methods; choosing the appropriate design

Design Principles

• Applying the principles of power and sample size, balance, replication, order, efficiency, randomisation and blocking, interaction, and confounding

Design and Analysis of Factorial Experiments

• Constructing full-factorial designs and applying computational and graphical methods to analyse and evaluate the significance of results
• Planning the experiment and determining the experimental objective.
• Explanation of the terminology – responses, factors, levels, replication, randomization, design points, design runs
• Understanding the statistical importance of avoiding excess variation in experiments – the role of measurement and careful control of the experiments
• Establishing the basic principles with a two factor and three factor design – explanation of main effects and interactions
• Analysis of experimental results using the two-sample t-test, ANOVA, and the probability plot
• Screening out the non-significant factors
• Understanding how to interpret interaction plots
• The role of blocking in DOE
• The need to reduce the number of runs when there are a large number of factors involved – the concept of using fractional factorial designs

Day 3

• Fractional factorial designs continued
• Simple and multiple regression and correlation analysis
• Analysis of residuals
• Optimization – Response Surface Methodology (RSM)– Modelling the relationship between factors and responses using advanced mathematical techniques and computer software
• Simultaneously optimising multiple responses