In this training program, attendees will understand when and why to apply DOE (design of experiments). They will also learn to identify and interpret significant factor effects and 2-factor interactions and develop predictive models to explain and optimize process/product behavior. Applying efficient fractional factorial designs in screening experiments will also be discussed.
Participants will gain a solid understanding of important concepts and methods in statistical experiments. Successful experiments allow the development of predictive models for the optimization of product designs or manufacturing processes. Several practical examples and case studies will be presented to illustrate the application of technical concepts. This webinar will prepare attendees to begin designing and conducting experiments. Attendees will also learn how to analyze the data from experiments to understand significant effects and develop predictive models utilized to optimize process behavior.
Design of Experiments has numerous applications, including:
Introduction to Experimental Design
Two Level Factorial Designs
Developing Mathematical Models
Fractional Factorial Designs (Screening)
Principal Statistician, Integral Concepts, Inc
Steven Wachs has 25 years of wide-ranging industry experience in both technical and management positions. Steve has worked as a statistician at Ford Motor Company where he has extensive experience in the development of statistical models, reliability analysis, designed experimentation, and statistical process control.
Steve is currently a Principal Statistician at Integral Concepts, Inc. where he assists manufacturers in the application of statistical methods to reduce variation and improve quality and productivity. He also possesses expertise in the application of reliability methods to achieve robust and reliable products as well as estimate and reduce warranty.
Experimentation is frequently performed using trial and error approaches which are extremely inefficient and rarely lead to optimal solutions. Furthermore, when it’s desired to understand the effect of multiple variables on an outcome (response), “one-factor-at-a-time” trials are often performed. Not only is this approach inefficient, it inhibits the ability to understand and model how multiple variables interact to jointly affect a response. Statistically based Design of Experiments provides a methodology for optimally developing process understanding via experimentation.
Registrants may cancel up to two working days prior to the course start date and will receive a letter of credit to be used towards a future course up to one year from date of issuance. FDATrainingAlert would process/provide refund if the Live Webinar has been cancelled. The attendee could choose between the recorded version of the webinar or refund for any cancelled webinar. Refunds will not be given to participants who do not show up for the webinar. On-Demand Recordings can be requested in exchange.
Webinar may be cancelled due to lack of enrolment or unavoidable factors. Registrants will be notified 24hours in advance if a cancellation occurs. Substitutions can happen any time.
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