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.
Participants gain a solid understanding of important concepts and methods in statistically based experimentation. Successful experiments allow the development of predictive models for the optimization of product designs or manufacturing processes. Several practical examples and case studies are presented to illustrate the application of technical concepts. This webinar will prepare you to begin designing and conducting experiments. You will also learn how to analyze the data from experiments to understand significant effects and develop predictive models utilized to optimize process behavior.
