This 4-day track provides participants with the skills needed to use various DOE techniques to effectively plan and analyze designed experiments. Participants will learn to identify the key factors that impact a critical quality measure and optimize both product results and process performance. Plus, they’ll 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. Analytical and statistical principles will be presented through real-world case studies, examples, and exercises.
This course is most 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.
Training Track
- Minitab Essentials
- Factorial Designs
- Response Surface Designs
- Workshop
DAYS 1-2
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.
Topics Include:
- Importing and Formatting Data
- Bar Charts
- Histograms
- Boxplots
- Pareto Charts
- Scatterplots
- Tables and Chi-Square Analysis
- Measures of Location and Variation
- t-Tests
- Proportion Tests
- Tests for Equal Variance
- Power and Sample Size
- Correlation
- Simple Linear and Multiple Regression
- One-Way ANOVA
- Multi-Variable ANOVA
Prerequisites: None

