Design for Six Sigma Yellow Belt (Foundations) Online Training

Course Structure

This self-paced course consists of 8 recorded sessions:

• • Full Factorial Designs (2 Levels)
    • What is a full factorial design?
    • Advantages and disadvantages of full factorials
    • Estimating effects and the meaning of interaction
    • Orthogonality and coding of design matrices
    • Graphical analysis of results (marginal means plots, interaction plots, Pareto of effects, surface/contour plots)
    • Statistical analysis of results (regression, p-values, R2 and Adjusted R2)
    • Prediction and optimization
    • Examples of Full Factorial designs and analysis using DOE Pro software

   

  • Fractional Factorial and Screening Designs (2 levels)
    • What are fractional factorial designs?
    • How to set up a fractional factorial design
    • Aliasing of effects
    • Resolution of a design
    • Advantages and disadvantages of fractional factorials
    • Reasons why experiments may fail to confirm
    • Other types of screening designs (Taguchi L12)
    • Recommended 2-level designs based on the number of factors, following a simple-to-use flowchart
    • Two-phase approach (screening and modeling)
    • Examples using DOE Pro software

   

  • Three Level Designs
    • Types of Input Factors: Qualitative versus Quantitative
    • Three-level design types:
      • Full factorial design
      • Taguchi L18 (screening)
      • Box Behnken design (modeling)
      • Central Composite design (modeling)
    • Keep it Simple (KISS) guidelines for choosing a design
    • Three-level design examples using DOE Pro software

   

  • DOE Rules of Thumb
    • Sample size for experimental designs
    • Choosing the best design for your application
    • Identifying significant terms using regression analysis
    • Rule of hierarchy when building models
    • Identifying significant terms in two-level s-models without p-values
    • Using R2, adjusted R2, and F value to quantify regression model strength
    • Using Tolerance for measuring orthogonality
    • 12 step process for planning and executing designed experiments
    • Regression output using DOE Pro software

   

   

  • Mixed Factor – Mixed Level Designs
    • What is a mixed factor – mixed level design?
    • How this differs from traditional designs and why we would use it
    • High Throughput Testing and a basic combinatorial relationship
    • Advantages and disadvantages of this category of designs
    • Using Pro-Test software to generate these designs and import them into DOE Pro for analysis
    • Mixed factor – mixed level design examples

   

  • Multiple Response Optimization
    • Illustrating multiple response examples using IPO diagrams
    • Approaches for multiple response optimization (graphical, analytical)
    • Using DOE Pro software to create and analyze designs with multiple responses
    • Multiple response optimization examples

   

  • Residual Analysis
    • Simple linear regression and regression diagnostics
    • What are residuals?
    • Residual plotting
    • Generating residuals and residual plots using DOE Pro software
    • Residual diagnostics (residuals, studentized residuals, R-studentized residuals, leverage, Cook’s D)
    • Outliers and influential observations
    • Lack of fit analysis

   

  • Historical Data Analysis
    • What is historical data?
    • The importance of the Tol values in regression analysis of historical data
    • Why coding of the data is necessary
    • How to set up and analyze historical data using DOE Pro software
    • Cautions when modeling with historical data
    • Need for graphical analysis when cleansing historical data
    • Examples and a case study of analyzing historical data

   

  At the end of each session, there is an associated 5-question quiz to test your knowledge and gauge your understanding of that topic.  To receive credit for module completion 4 out of 5 quiz questions need to be answered correctly.  Quizzes can be taken until a passing score is achieved.

As with all of our training, the objective of this course is to develop a comprehensive set of skills that will let you function effectively as a change agent in your company. You will learn the Design for Six Sigma body of knowledge, practice techniques for both quantitative and non-quantitative analysis, and gain the skills necessary to become a practitioner of the Design for Six Sigma methodology and toolset to prepare you to lead Design for Six Sigma studies within your organization.