Participants gain a solid understanding of important concepts and methods to analyze data statistics training courses and support effective decision making. Many practical examples are presented to illustrate the application of technical concepts.
Many engineers, scientists, and business statistics training analysts struggle with the application of statistical methods when analyzing data to making decisions. Frequently, engineers and scientists react to data without considering the natural variation that exists. Non statisticians frequently seek help in tasks such as determining appropriate sample sizes, interpreting tests results, distinguishing statistical differences from practical differences, and developing predictive models. This courseprovides an in-depth treatment of statistical analysis methods to support decision making. The focus is on data organization, graphical methods, hypothesis testing, and predictive modeling.
Participants gain a solid understanding of important concepts and methods to develop predictive models that allow the optimization of product designs or manufacturing processes. Many practical examples are presented to illustrate the application of technical concepts. Participants also get a chance to apply their knowledge by designing an experiment, analyzing the results, and utilizing the model(s) to develop optimal solutions (in the 4-days Virtual DOE Training program). Minitab or other statistical software is utilized in the class.
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.
In this course, 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 course will prepare you to design and conduct effective 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.