Design of Experiments with Minitab

This course is available for virtual delivery – please contact us for further details (1 face-to-face training day typically translates into 2 to 4 virtual sessions per day, this is determined by the specific course content. Number of sessions and specific session times will be confirmed in advance of course delivery.)   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.

Delivery Mode

  • Public

    03 - 05 Nov 2020
    21 - 25 Jul 2020
  • Customised

    Contact Us


What's covered?

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

Who should participate?

  • Product design and process design engineers and scientists
  • R&D engineers and scientists
  • QC and QA personnel

What will I learn?

Participants achieve the following learning outcomes from the programme;

  • 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

Who are the tutors?

Albert Plant

With a background of twenty year’s practical experience, Albert Plant is Ireland’s leading trainer and consultant in the application of statistics in manufacturing, industry and business. Combining Degrees in Engineering and Statistics, he brings a unique blend of a deep understanding of manufacturing technology with expertise in all aspects of statistical and data analysis to his training courses.
Attendees on Albert’s training courses include people, many with PhDs, undertaking the most advanced research and development in pharmaceuticals, medical devices, nanotechnology and other leading-edge specialities, who require statistical skills to understand the outcome of their research work.

Many customers require that the training courses be customised and Albert has developed a high level of expertise in meeting this requirement. His In-House courses typically incorporate company data and both the length and content of the courses are agreed in advance with the customer to meet very specific needs.

Minitab is the leading brand of general purpose statistical software and Albert has a twenty year association with Minitab. He contributed to the development of the Acceptance Sampling module in Minitab and, as a volunteer tester for new versions of the software, he was involved in the testing of version 17 prior to its release in 2014. He also has a high level of expertise in other statistical software, such as Design Expert, which he uses in Design of Experiments.

Dick Delaney

Dick Delaney, a Fellow of the Academy of Biomedical Science, started his career in Clinical Chemistry working in a number of hospitals in Ireland before being seconded to the World Health Organization in Africa.  In 1980 he moved to Galway Mayo Institute of Technology as lecturer and course co-ordinator in Medical Laboratory Science. In 1992 Dick completed an MSc in Applied Statistics and has since been involved in lecturing on the statistical aspects of Quality Management to Diploma and Degree students.  Through links with the pharmaceutical and medical device industry he has been responsible for a number of projects in process control and improvement, uncertainty estimation and SPC training.  One of Dick’s main strengths is his ability to explain complex statistical concepts in simple everyday language. He believes in keeping the theory to a minimum, the emphasis being on practical applications using statistical software.

Dick has extensive experience in the quality tools involved in problem solving and working in teams to resolve.

Grainne Heneghan

Grainne is a highly experienced Continuous Improvement and Lean Six Sigma Master Black Belt with over 20 years’ experience in the global IT industry.

Grainne has 11 years’ experience of developing and delivering training and working with Quality and Lean Six Sigma methodologies, tools and techniques, with focus on improving processes, reducing waste and decreasing costs for teams and organisations. She is resourceful and adaptable and uses strong communication and interpersonal skills along with metrics, data and logic to ensure that teams achieve their goals and successfully implement change within their organisations.

Grainne has trained and coached many Yellow, Green and Black Belt students to successful certification. Along with many years training in a traditional classroom style, she spent 4 years training using an online Virtual Classroom while working at Hewlett Packard. This included virtual training in Minitab and Statistical Concepts alongside softer skills such as Brainstorming for Root Cause Analysis and How to Complete FMEAs. She likes to blend her efficient organising skills and strong attention to detail with a softer empathetic and practical approach to projects. She is open-minded with a holistic and positive approach when working with teams.

Grainne enjoys working with teams and training students to use and to get the most from tools such as Minitab, and other statistical methods and techniques.

What are the 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.

How do we train and support you?

In-House Courses
For In-House courses the tutor will contact you in advance to discuss the course programme in more detail in order to tailor it specifically for your organisation. Where appropriate and facilitated by the organisation, the course can be run using Minitab or Design Expert software.

Course Manual
Delegates will receive a very comprehensive course manual written by the course tutor, which explains the underlying statistics, describes the principles of experimental design, explains in detail how experiments are designed and analysed, includes examples of several practical case studies, and incorporates completed versions of all the course exercises and graphs, including the output from Minitab computer software.  The course manual will provide a very useful reference for participants undertaking the design and analysis of experiments when they return to their workplace.

What software do we use?

Minitab will be demonstrated as part of the training.    Delegates are invited to bring a laptop loaded with either Minitab 17, 18 or 19 and they will work through several Minitab exercises throughout the three days of the course.   A free 30 day trial version of Minitab 19 is available on   For in-company training courses, there is an option to use the specialist DOE software, Design Expert.  A free 45 day trial version of Design Expert is available for downloading from

Grainne Heneghan

Duration: 3 daysPublic Price: €1,150
  • 03 - 05 Nov 2020
    Location: Dublin Book Date

Grainne Heneghan

Duration: 3 daysVirtual Training Price: €995
  • 21 - 25 Jul 2020
    Virtual Training Book Date

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