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Design of experiments

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An introduction to SigmaXL's Design of Experiments tools. …

An introduction to SigmaXL's Design of Experiments tools.

Established in 1998, SigmaXL Inc. is a leading provider of user friendly Excel Add-ins for Lean Six Sigma graphical and statistical tools and Monte Carlo simulation.

SigmaXL® customers include market leaders like Agilent, Diebold, FedEx, Microsoft, Motorola and Shell. SigmaXL® software is also used by numerous colleges, universities and government agencies.

Our flagship product, SigmaXL®, was designed from the ground up to be a cost-effective, powerful, but easy to use tool that enables users to measure, analyze, improve and control their service, transactional, and manufacturing processes. As an add-in to the already familiar Microsoft Excel, SigmaXL® is ideal for Lean Six Sigma training and application, or use in a college statistics course. 

DiscoverSim™ enables you to quantify your risk through Monte Carlo simulation and minimize your risk with global optimization. Business decisions are often based on assumptions with a single point value estimate or an average, resulting in unexpected outcomes.

DiscoverSim™ allows you to model the uncertainty in your inputs so that you know what to expect in your outputs.

Published in: Business

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  • 1. Design of Experiments SigmaXL® Version 6.1 DOE Overview Basic DOE Templates 2-Level Factorial and Plackett-Burman Screening Designs Example: 3-Factor, 2-Level Full-Factorial Catapult DOE Main Effects & Interactions Plots Analyze 2-Level Factorial and Plackett-Burman Screening Designs Analyze Catapult DOE Predicted Response Calculator Response Surface Designs Contour & 3D Surface Plots
  • 2. Design of Experiments  Basic DOE Templates      Automatic update to Pareto of Coefficients Easy to use, ideal for training Generate 2-Level Factorial and PlackettBurman Screening Designs Main Effects & Interaction Plots Analyze 2-Level Factorial and PlackettBurman Screening Designs Back to Index
  • 3. Basic DOE Templates Back to Index
  • 4. Design of Experiments: Generate 2-Level Factorial and Plackett-Burman Screening Designs      User-friendly dialog box 2 to 19 Factors 4,8,12,16,20 Runs Unique “view power analysis as you design” Randomization, Replication, Blocking and Center Points Back to Index
  • 5. Design of Experiments: Generate 2-Level Factorial and Plackett-Burman Screening Designs View Power Information as you design! Back to Index
  • 6. Design of Experiments Example: 3-Factor, 2-Level Full-Factorial Catapult DOE Objective: Hit a target at exactly 100 inches! Back to Index
  • 7. Design of Experiments: Main Effects and Interaction Plots Back to Index
  • 8. Design of Experiments: Analyze 2-Level Factorial and Plackett-Burman Screening Designs     Used in conjunction with Recall Last Dialog, it is very easy to iteratively remove terms from the model Interactive Predicted Response Calculator with 95% Confidence Interval and 95% Prediction Interval. ANOVA report for Blocks, Pure Error, Lack-offit and Curvature Collinearity Variance Inflation Factor (VIF) and Tolerance report Back to Index
  • 9. Design of Experiments: Analyze 2-Level Factorial and Plackett-Burman Screening Designs     Residual plots: histogram, normal probability plot, residuals vs. time, residuals vs. predicted and residuals vs. X factors Residual types include Regular, Standardized, Studentized (Deleted t) and Cook's Distance (Influence), Leverage and DFITS Highlight of significant outliers in residuals Durbin-Watson Test for Autocorrelation in Residuals with p-value Back to Index
  • 10. Design of Experiments Example: Analyze Catapult DOE Pareto Chart of Coefficients for Distance 25 Abs(Coefficient) 20 15 10 5 B: BC AB AC AB C St op Pi n gh t He i C: Pi n A: Pu ll B ac k 0 Back to Index
  • 11. Design of Experiments: Predicted Response Calculator Excel’s Solver is used with the Predicted Response Calculator to determine optimal X factor settings to hit a target distance of 100 inches. 95% Confidence Interval and Prediction Interval Back to Index
  • 12. Design of Experiments: Response Surface Designs    2 to 5 Factors Central Composite and Box-Behnken Designs Easy to use design selection sorted by number of runs: Back to Index
  • 13. Design of Experiments: Contour & 3D Surface Plots Back to Index

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