In Design of Experiments, blocking involves recognizing uncontrolled factors and ensuring as wide a spread across these factors.
Confounding occurs when you can’t distinguish the effects of certain factor interactions because of other potential factor effects.
Yates analysis is a method of analyzing data from full and partial factorial design experiments. It requires two levels for each factor in the experiment.
In his latest article Jeremy provides great real-life examples how performing root cause analysis can change lives.
Design of Experiments is a way to intelligently form decision frameworks on how to improve a process. This is done by determine what factors into a process.