Process Control and Capability

This 4-day track provides participants with a comprehensive toolkit for understanding and applying SPC. The track covers the data analysis techniques necessary to validate if a measurement system is reliable, plus teaches how to determine if a process is stable and how to quantify if a process is capable of meeting customer specifications.

Participants will learn how to understand and apply SPC by covering the data analysis techniques necessary to validate if a measurement system is reliable, determine if a process is stable, and quantify if a process is capable of meeting customer specifications. Analytical and statistical principles will be presented through real-world case studies, examples, and exercises.

 

This course is most appropriate for QA managers, CQI coordinators, process engineers, and other quality professionals who need to understand SPC and how each of the tools are integrated into successful manufacturing processes.

Training Track

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
Summary Report for SupplrA
Interaction Plot for PntWear - Data Means