Designing effective experiments- principles of experimental design
- 2 days
Modern analytical methods and production processes (manufacturing and research) are complex,
with many different factors affecting the outcome. In order to be competitive, companies need to
minimize resources expended on development and maximize process performances. 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 or the regulatory agencies worldwide (including, for example, US Food and
Drug Administration). DoE provides a well structured method for determining the relationships between factors affecting the process under study.
Download the course programme.
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 experiment design
- Choose the most efficient experiment for each problem
- Understand how to interpret the results.
The course will cover:
- Designs for optimisation – basic response surface models
- Efficient screening experiments using fractional factorial designs
- Studying multiple effects – factorial designs
- Strategies for reducing nuisance effects- randomisation, paired experiments and blocked designs
- Estimating sample size for simple designs
- Simple experiments for single effects
- Doing the right experiment – setting objectives
- Analysing and interpreting designed experiments.
Who should attend?
The course is aimed at analytical chemists 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.