Reduce Experimental Runs via Fractional Factorial Designs

Save time and costs by utilizing smaller designs! In this webinar Stat-Ease consultant, Shari Kraber, reveals the information provided by both regular-fraction versus more-modern minimum-run designs-a Stat-Ease invention. Take away a clear guide for selecting the best design based on your factorial DOE objective: screening or characterization.
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[@DM798]

Thnak you for this amazing video! How can I choose the number of samples I have to run for each trial?

[@StatisticsMadeEasybyStatEase]

Great question - this is a power/sample size calculation, based on the change in the response that you want to detect and the current standard deviation of the response. If you have a current license of Design-Expert or Stat-Ease 360, you are welcome to email more details to [e-mail]. If you don't want to do the calculations, then often using a sample size of 4-5 per run, and then entering the average of those runs as the response, will give sufficient information.