Design of Experiments (DOE)
Passive data collection leads to a number of problems in statistical modeling. Observed changes in a response variable may be correlated with, but not caused by, observed changes in individual factors (process variables). Simultaneous changes in multiple factors may produce interactions that are difficult to separate into individual effects. Observations may be dependent, while a model of the data considers them to be independent.
Designed experiments address these problems. In a designed experiment, the data-producing process is actively manipulated to improve the quality of information and to eliminate redundant data. A common goal of all experimental designs is to collect data as parsimoniously as possible while providing sufficient information to accurately estimate model parameters.
Fractional Factorial Designs
Latin Hypercube Designs
- Full Factorial Designs
Designs for all treatments
- Fractional Factorial Designs
Designs for selected treatments
- Response Surface Designs
Quadratic polynomial models
- Improve an Engine Cooling Fan Using Design for Six Sigma Techniques
This example shows how to improve the performance of an engine cooling fan through a Design for Six Sigma approach using Define, Measure, Analyze, Improve, and Control (DMAIC).
- D-Optimal Designs
Minimum variance parameter estimates