advertisement

advertisement

Information about Design of experiments

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.

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.

advertisement

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

Basic DOE Templates Back to Index

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

Design of Experiments: Generate 2-Level Factorial and Plackett-Burman Screening Designs View Power Information as you design! Back to Index

Design of Experiments Example: 3-Factor, 2-Level Full-Factorial Catapult DOE Objective: Hit a target at exactly 100 inches! Back to Index

Design of Experiments: Main Effects and Interaction Plots Back to Index

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

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

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

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

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

Design of Experiments: Contour & 3D Surface Plots Back to Index

Canvas Prints at Affordable Prices make you smile.Visit http://www.shopcanvasprint...

30 Días en Bici en Gijón organiza un recorrido por los comercios históricos de la ...

Con el fin de conocer mejor el rol que juega internet en el proceso de compra en E...

With three established projects across the country and seven more in the pipeline,...

Retailing is not a rocket science, neither it's walk-in-the-park. In this presenta...

The design of experiments (or experimental design) is the design of any task that aims to describe or explain the variation of information under conditions ...

Read more

Die statistische Versuchsplanung (englisch design of experiments, DoE) wird bei Entwicklung und Optimierung von Produkten oder Prozessen eingesetzt.

Read more

Design of Experiments (DOE) Outline. Introduction; Preparation; Components of Experimental Design; Purpose of Experimentation; Design Guidelines; Design ...

Read more

Design Of Experiments 29 Pl k BDi gp Plackett-Burman Designs • Extremfall des Fractional Factorial Designs • III-Fractional Factorial DesignsFractional ...

Read more

Ziel von DoE ist es, mit möglichst wenig Versuchsaufwand möglichst viel über die Zusammenhänge von Einflussvariablen (Inputs) und Ergebnissen (Outputs) zu

Read more

Design of experiments deals with planning, conducting, analyzing and interpreting controlled tests to evaluate the factors that control the value of a ...

Read more

Design of experiments (DOE) is a systematic method to determine the relationship between factors affecting a process and the output of that process.

Read more

Design of Experiments – A Primer. Understanding the terms and concepts that are part of a DOE can help practitioners be better prepared to use the ...

Read more

How to apply the statistical method called Design of Experiments (DOE) for quality improvement and research.

Read more

Design of Experiment is a method regarded as the most accurate and unequivocal standard for testing a hypothesis.

Read more

## Add a comment