Conducting Experiments and Analyzing Results in Six Sigma

Candidates seeking Six Sigma Black Belt certification, quality professionals, engineers, production managers, frontline supervisors, and all individuals charged with responsibility for improving quality and processes at the organizational or departmental level, including process owners and champions

Proficiency at the Green Belt level with DOE terminology, principles, and concepts; linear regression; and analysis of variance (ANOVA) as scoped in the ASQ – Six Sigma Green Belt Body of Knowledge (BOK)

Expected Duration
120 minutes

Six Sigma teams design and conduct experiments to investigate the relationships between input variables and response variables. By controlling and changing the input variables and observing the effects on the response variables, a Six Sigma team gains a deep understanding of these relationships. After determining what and how much needs to be changed to meet the desired improvement, teams generate solution ideas based on the best combination of input variables’ settings to optimize the response, and then the ideas are tested, implemented, and validated. Later in the Control stage, efforts are made to keep the improved processes, products, or services under statistical control and to retain the gains.
This course explores full and fractional factorial designs and the DOE process. In addition, it teaches how to select, test, and validate solutions using a variety of analysis, screening, and testing tools commonly used in Six Sigma. This course is aligned with the ASQ Certified Six Sigma Black Belt certification exam and is designed to assist learners as part of their exam preparation.


Full Factorial Experiments

  • determine whether a chosen design is a full factorial design that can meet resolution requirements, in a given scenario
  • recognize the characteristics of an experiment, represented by a given run table
  • calculate the number of runs in a given experiment
  • calculate an estimate of a main effect in a full factorial experiment
  • based on results from a full factorial experiment, recognize which terms should be included in the model
  • Fractional Factorial Experiments

  • recognize circumstances suitable for a fractional factorial design
  • recognize the design implications of a proposed fractional factorial experiment
  • interpret an interaction plot
  • One-factor Experiments

  • identify conditions that recommend a randomized block design
  • identify the trial pattern that will fully randomize a given block design
  • identify the characteristics of Latin square designs
  • recognize which experimental factors are significant in the results of a Latin square design




    Multi-license discounts available for Annual and Monthly subscriptions.