It is appropriate for design engineers, scientists, R&D team members, process engineers, and other quality professionals who want to use a cost-effective and organized approach to conducting industrial experiments.
Gain exposure to the data analysis techniques necessary to select the appropriate design, identify key factors that impact a critical quality measure, and optimize product results and process performance.
Statistical principles will be presented through real-world examples and exercises ― all supported by Minitab Statistical Software.
In this 2-day foundational course you will learn to minimize the time required for data analysis by using Minitab to import data, develop sound statistical approaches to exploring data, create and interpret compelling graphs, and export results. Analyze a variety of real world data sets to learn how to align your applications with the right statistical tool, and interpret statistical output to reveal problems with a process or evidence of an improvement. Learn the fundamentals of important statistical concepts, such as hypothesis testing and confidence intervals, and how to uncover and describe relationships between variables with statistical modeling tools.
This course places a strong emphasis on making sound decisions based upon the practical application of statistical techniques commonly found in manufacturing, engineering, and research and development endeavors.
None. This course is a prerequisite for all other general Minitab courses.
Learn to generate a variety of full and fractional factorial designs using Minitab’s intuitive DOE interface. Real-world applications demonstrate how the concepts of randomization, replication, and blocking form the basis for sound experimentation practices. Develop the skills necessary to correctly analyze resulting data to effectively and efficiently reach experimental objectives.
Use Minitab’s customizable and powerful graphical displays to interpret and communicate experimental results to improve products and processes, find critical factors that impact important response variables, reduce process variation, and expedite research and development projects.
Expand your knowledge of basic 2 level full and fractional factorial designs to those that are ideal for process optimization. Learn how to use Minitab’s DOE interface to create response surface designs, analyze experimental results using a model that includes quadratics, and find optimal factor settings.
Learn how to experiment in the real world by using techniques such as sequential experimentation that balance the discovery of critical process information while being sensitive to the resources required to obtain that information. Learn how to find factor settings that simultaneously optimize multiple responses.
Minitab Essentials and Factorial Designs