Historical Data 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 [...]

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Lean Six Sigma Summary

Topics Discussed 3 primary ingredients for Success in a LSS initiative (people, projects, prioritization) Lean Six Sigma deployment and the infrastructure Importance of Knowledge and using questions as the “pull” system for delivering value Types of projects (rapid improvement events, DMAIC projects) Prioritizing and selecting projects Project execution and tracking progress Realizing project benefits [...]

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Residual Analysis

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

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Multiple-Response Optimization

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

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Mixed Factor -Mixed Level Designs

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 [...]

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DOE Rules of Thumb

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 [...]

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3-Level Designs

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

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Fractional Factorial and Screening Designs (2-levels)

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 [...]

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Full Factorial Designs (2-levels)

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) [...]

By |2022-06-15T12:56:17+00:00November 13th, 2020|Comments Off on Full Factorial Designs (2-levels)

Intro to DOE

Introduction to DOE The What and Why of DOE Who should use DOE? DOE terminology Reasons for using DOE (screening, modeling, performance validation and verification) The four pillars of DOE History and evolution of DOE along with key contributors Introduction to DOE Pro software

By |2022-06-15T12:56:09+00:00November 13th, 2020|Comments Off on Intro to DOE
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