Design of Experiments - Fundamentals
Design of Experiments (DOE) is a procedure to planning, designing and analysing experiments to effectively and efficiently draw valid and statistically sound conclusions.
Description
This 1 day course aims to:
- Familiarise delegates with DOE principles and terminology (randomisation, replication, blocking, factors, effects, interactions, ANOVA, hypothesis testing)
- Provide statistical background theory in order to understand DOE process and development
- Demonstrate the steps of a full factorial experiment design in order to optimise response to key factors
- Analysis of results using hypothesis test plots and regression modelling
- Explain the purpose of non full factorial designs
By the end of the course, delegates should be able to:
- Define key terminology (factors, effects, interactions, anova, hypothesis testing)
- Explain key principles (randomisation, replication, blocking,)
- Calculate Coefficients of a simple linear regression model
- Describe the steps in designing a full factorial experiment
- Create a coded 3 factor 2 level full factorial design matrix
- Perform a hypothesis test
- Perform an analysis of variance
- Interpret common DOE plots (interaction effects, cube plots, Normal Probability Plots, Contour Plots, Pareto Charts, Optimal Solutions)
- Justify the choice of experiment design approaches
Who Should Attend?
This course is intended as an introduction to the subject for delegates that are looking to incorporate Design of Experiment approaches into their engineering analysis.
Prerequisites
The course assumes no prior knowledge of the analysis itself, but delegates should be familiar with statistical approaches and OFAT experimentation.
Duration
1 Day
The course commences at 9:00am and finishes at approximately 5:00pm.
Course Fee
From £525, including lunch and refreshments.
Agenda
Sessions
Introduction
- What is DOE
- Terminology
- Basic Principles
General full factorial designs
- Analysis Process
- Building a Design Matrix
- Recording Responses
Analysis Methods
- Plots
- Regression Modelling
- Hypothesis Testing
- Analysis of Variance
Alternative Designs
- Common Alternative Designs
- Design Resolutions