Design of experiments

Learn about the fundamental uses of DOE (screening, optimization and robustness testing) and how these applications can generate value from your data. Follow a demo on the MODDE software showing the essential steps for design generation, model building, model tuning, and decision making from response contour plots.

[@pelee57]

great video - really loved the popcorn analogy

[@neilbryanclosa462]

Great lecture! Where could I watch the other parts of the series?

[@piapsalm7669]

Hello Sir, newbie here...where did you get the data in excel, specifically the Kernels and Taste?Response measurement does it mean the actual result of the experiment?

[@ehsanshamloo]

@16:17 you mentioned, "after some range-finding experiments"! what do you exactly mean by this? Do we need to run some experiments to find the linear range of the responses to each factor before using the DOE?

[@Umetrics]

An essential part of DOE is that the experiments will lead to good results, which means that the outcome it compliant to what needs to be achieved. One way of doing this is to run some experiments with extreme to condition to see if the outcome is still ok or reasonable. That can help to find optimal starting conditions for the DOE. The alternative way would be the use of prior knowledge to define good starting conditions for the DOE and start with a screening design instead.
Sartorius Data Analytics Support team

[@alecpokrandt3322]

this popcorn is delicious