Designing effective experiments – principles of experimental design
This course can be delivered as an onsite course. To discuss your requirements, please contact us.
Modern analytical methods and production processes used in research and manufacturing are complex, with many different factors affecting the outcome. In order to be competitive, companies need to minimise resources expended on development and maximise process performance. Design of Experiments (DoE) enables these complex situations to be understood, reducing the cost of gaining an in-depth knowledge of the process which can be translated into competitive advantage. It is also a tool required by most of the regulatory agencies worldwide (including, for example, the US Food and Drug Administration). DoE provides a well-structured method for determining the relationships between factors affecting the process under study.
What are the benefits?
This course will help you:
- Develop a systematic approach to experiments for scientific research, method development and product improvement
- Recognise and understand the advantages of the main types of experimental design
- Choose the most efficient experiment for each problem
- Understand how to interpret the results
- Apply the principles of experimental design during laptop-based workshops.
The course will cover:
- Doing the right experiment – setting objectives
- Simple experiments for single effects
- Estimating sample size for simple designs
- Strategies for reducing nuisance effects – randomisation, paired experiments and blocked designs
- Studying multiple effects – factorial designs
- Efficient screening experiments using fractional factorial designs
- Designs for optimisation – basic response surface models
- Analysing and interpreting designed experiments.
Who should attend?
The course is aimed at analytical scientists and laboratory managers who need to plan experimental studies for analytical method development, or for product or process improvement, and who wish to develop an understanding of the main types of experiment used in industrial experimental design. The course is suitable for those who have a basic knowledge of statistics, including a basic understanding of significance testing, linear regression and analysis of variance.