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.
- This webinar will teach you a methodology to perform experiments in an optimal fashion and review the common types of experimental designs and techniques. You will learn to develop predictive models to describe the effects that variables have on one or more responses and utilize predictive models to develop optimal solutions.
Areas Covered in the Seminar:
- Motivation for Structured Experimentation (DOE).
- DOE Approach / Methodology.
- Types of Experimental Designs and their Applications.
- DOE Techniques.
- Developing Predictive Models.
- Using Models to Develop Optimal Solutions.
- Case Study