book

JMP 11 Design of Experiments Guide

Name: JMP 11 Design of Experiments Guide
Author: SAS Institute
ISBN: 9781612906874

by SAS Institute

September 2013

Intermediate to advanced

402 pages

9h 30m

English

SAS Institute

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Contents
Learn about JMP
Documentation and Additional Resources
Formatting Conventions
JMP DocumentationJMP Documentation LibraryJMP HelpAdditional Resources for Learning JMPTutorialsSample Data TablesLearn about Statistical and JSL TermsLearn JMP Tips and TricksTooltipsJMP User CommunityJMPer CableJMP Books by UsersThe JMP Starter Window
Introduction to Designing Experiments
A Beginner’s Tutorial
About Designing Experiments
My First ExperimentThe SituationStep 1: Design the ExperimentDefine the Responses: Popped Kernels and Total KernelsDefine the Factors: Time, Power, and BrandStep 2: Define Factor ConstraintsStep 3: Add Interaction TermsStep 4: Determine the Number of RunsStep 5: Check the DesignStep 6: Gather and Enter the DataStep 7: Analyze the Results
Building Custom Designs
The Basic Steps
Creating a Custom Design
Enter Responses and Factors into the Custom DesignerHow to Enter ResponsesSpecifying Response Goal Types and Lower and Upper LimitsUnderstanding Response Importance WeightsAdding Simulated Responses, If DesiredHow to Enter FactorsFactors that are Easy, Hard, or Very Hard, to Change: Creating Optimal Split-Plot and Split-Split-Plot DesignsDefining Factor Constraints, If NecessaryDescribe the ModelAdding Terms to the ModelEstimability: Necessary and If Possible TermsSpecifying Alias TermsSelect the Number of RunsThe Design ReportUnderstanding Design EvaluationPower AnalysisThe Prediction Variance ProfileThe Fraction of Design Space PlotThe Prediction Variance SurfaceEstimation EfficiencyThe Alias Matrix (Confounding Pattern)Color Map on CorrelationsThe Design Diagnostics TableSpecify Output OptionsMake the JMP Design TableCreating Random Block DesignsCreating Split Plot DesignsCreating Split-Split Plot DesignsCreating Strip Plot DesignsSpecial Custom Design CommandsSave Responses and Save FactorsLoad Responses and Load FactorsSave Constraints and Load ConstraintsSet Random Seed: Setting the Number GeneratorSimulate ResponsesSave X MatrixOptimality CriterionNumber of Starts: Changing the Number of Random StartsWhy Change the Number of Starts?Default Choice of Number of Random Starts: Technical InformationSphere Radius: Constraining a Design to a HypersphereDisallowed Combinations: Accounting for Factor Level RestrictionsAdvanced Options for the Custom DesignerAltering the Mixture SumSplit Plot Variance RatioPrior Parameter VarianceD Efficiency WeightSet Delta for PowerSave Script to Script WindowAssigning Column PropertiesDefine Low and High Values (DOE Coding) for ColumnsSet Columns as Factors for Mixture ExperimentsDefine Response Column ValuesAssign Columns a Design RoleIdentify Factor Changes Column PropertyHow Custom Designs Work: Behind the Scenes
Examples Using the Custom Designer
Creating Screening Experiments
Creating a Main-Effects-Only Screening DesignCreating a Screening Design to Fit All Two-Factor InteractionsA Compromise Design Between Main Effects Only and All InteractionsCreating “Super” Screening DesignsThe Need for Supersaturated DesignsExample: Twelve Factors in Eight RunsScreening Designs with Flexible Block SizesChecking for Curvature Using One Extra RunCreating Response Surface ExperimentsExploring the Prediction Variance SurfaceIntroducing I-Optimal Designs for Response Surface ModelingA Three-Factor Response Surface DesignResponse Surface with a Blocking FactorCreating Mixture ExperimentsMixtures Having Nonmixture FactorsExperiments that are Mixtures of MixturesSpecial Purpose Uses of the Custom DesignerDesigning Experiments with Fixed Covariate FactorsCreating a Design that is Robust to a Linear Time Trend in the ResponseCreating a Design with Two Hard-to-Change Factors: Split PlotTechnical Discussion
Definitive Screening Designs

Definitive Screening Platform Overview
Example of Definitive ScreeningExample of Definitive Screening versus Plackett-Burman Designs
Screening Designs
Overview
Screening Design Examples
A Standard Design with Two Continuous Factors and One Categorical FactorA Standard Design for Five Continuous FactorsCreating a Screening DesignEnter ResponsesSpecifying Goal Types and Lower and Upper LimitsUnderstanding Importance WeightsEnter FactorsTypes of FactorsChoose a DesignDesign ListDisplay and Modify DesignAliasing of EffectsLook at the Confounding PatternUnderstanding Design CodesChanging the Coded DesignDesign GenerationDesign EvaluationSpecify Output OptionsView the Design TableCreating a Plackett-Burman designCreating a Main Effects Screening DesignAnalyzing Screening DataComparing Screening and Fit ModelLaunch the Screening PlatformThe Screening ReportContrastsHalf Normal PlotUsing the Fit Model PlatformThe Actual-by-Predicted PlotThe Scaled Estimates ReportA Power AnalysisAdditional Screening Analysis ExamplesAnalyzing a Plackett-Burman DesignAnalyzing a Supersaturated DesignStatistical DetailsOperationScreening as an Orthogonal RotationLenth’s Pseudo-Standard Error
Response Surface Designs
A Box-Behnken Design: The Tennis Ball Example
The Prediction ProfilerA Response Surface Plot (Contour Profiler)Geometry of a Box-Behnken DesignCreating a Response Surface DesignEnter Responses and FactorsChoose a DesignBox-Behnken DesignsCentral Composite DesignsSpecify Axial Value (Central Composite Designs Only)Specify Output OptionsView the Design Table
Full Factorial Designs
The Five-Factor Reactor Example
Analyze the Reactor DataCreating a Factorial DesignEnter Responses and FactorsSelect Output OptionsMake the Table
Mixture Designs
Mixture Design Types
The Optimal Mixture DesignAdding Effects to the ModelThe Simplex Centroid DesignCreating the DesignSimplex Centroid Design ExamplesThe Simplex Lattice DesignThe Extreme Vertices DesignCreating the DesignAn Extreme Vertices Example with Range ConstraintsAn Extreme Vertices Example with Linear ConstraintsExtreme Vertices Method: How It WorksThe ABCD DesignThe Space Filling DesignA Space Filling Example with a Linear ConstraintCreating Ternary PlotsFitting Mixture DesignsWhole Model Tests and Analysis of Variance ReportsUnderstanding Response Surface ReportsA Chemical Mixture ExampleCreate the DesignAnalyze the Mixture ModelThe Prediction ProfilerThe Mixture ProfilerA Ternary Plot of the Mixture Response Surface
Discrete Choice Designs
Introduction
Create an Example Choice ExperimentAnalyze the Example Choice ExperimentDesign a Choice Experiment Using Prior InformationAdminister the Survey and Analyze ResultsInitial Choice Platform AnalysisFind Unit Cost and Trade Off Costs with the Profiler
Space-Filling Designs
Introduction to Space-Filling Designs
Sphere-Packing DesignsCreating a Sphere-Packing DesignVisualizing the Sphere-Packing DesignLatin Hypercube DesignsCreating a Latin Hypercube DesignVisualizing the Latin Hypercube DesignUniform DesignsComparing Sphere-Packing, Latin Hypercube and Uniform MethodsMinimum Potential DesignsMaximum Entropy DesignsGaussian Process IMSE Optimal DesignsFast Flexible Filling DesignsCreating a Constrained Fast Flexible Filling DesignVisualizing the Fast Flexible Filling DesignBorehole Model: A Sphere-Packing ExampleCreate the Sphere-Packing Design for the Borehole DataGuidelines for the Analysis of Deterministic DataResults of the Borehole Experiment
Accelerated Life Test Designs
Designing Experiments for Accelerated Life Tests
Overview of Accelerated Life Test Designs
Using the ALT Design PlatformPlatform OptionsExample
Nonlinear Designs
Examples of Nonlinear Designs
Using Nonlinear Fit to Find Prior Parameter EstimatesCreating a Nonlinear Design with No Prior DataCreating a Nonlinear DesignIdentify the Response and Factor Column with FormulaSet Up Factors and Parameters in the Nonlinear Design DialogEnter the Number of Runs and Preview the DesignMake Table or Augment the TableAdvanced Options for the Nonlinear Designer
Taguchi Designs
The Taguchi Design Approach
Taguchi Design ExampleAnalyze the DataCreating a Taguchi DesignDetail the Response and Add FactorsChoose Inner and Outer Array DesignsDisplay Coded DesignMake the Design Table
Evaluating Experimental Designs
Using the Evaluate Design Platform
Launching the Evaluate Design Platform
The Evaluate Design ReportDesign EvaluationPower AnalysisPrediction Variance ProfileFraction of Design Space PlotPrediction Variance SurfaceEstimation EfficiencyAlias MatrixColor Map on CorrelationsDesign DiagnosticsExamplesAssessing the Impact of Lost RunsAssessing the Impact of Changing the Model
Augmented Designs
A D-Optimal Augmentation of the Reactor Example
Analyze the Augmented DesignCreating an Augmented DesignReplicate a DesignAdd Center PointsCreating a Foldover DesignAdding Axial PointsAdding New Runs and TermsSpecial Augment Design CommandsSave the Design (X) MatrixModify the Design Criterion (D- or I- Optimality)Select the Number of Random StartsSpecify the Sphere Radius ValueDisallow Factor Combinations
Prospective Sample Size and Power
Launching the Sample Size and Power Platform
One-Sample and Two-Sample MeansSingle-Sample MeanPower versus Sample Size PlotPower versus Difference PlotSample Size and Power Animation for One MeanTwo-Sample MeansPlot of Power by Sample Sizek-Sample MeansOne Sample Standard DeviationOne Sample Standard Deviation ExampleOne-Sample and Two-Sample ProportionsOne Sample ProportionOne-Sample Proportion Window SpecificationsTwo Sample ProportionsTwo Sample Proportion Window SpecificationsCounts per UnitCounts per Unit ExampleSigma Quality LevelSigma Quality Level ExampleNumber of Defects Computation ExampleReliability Test Plan and DemonstrationReliability Test PlanExampleReliability DemonstrationExample
References
Index
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Content preview from JMP 11 Design of Experiments Guide

Chapter 15

Evaluating Experimental Designs

Using the Evaluate Design Platform

Use the Evaluate Design to analyze the diagnostics of an existing experimental design. Evaluate your designs to see the impact of a lost run or to assess the estimability of model effects. This information can help you understand the strengths or limitations of your experimental data. From the results, you can change the model terms and the aliasing terms. Then the diagnostics update accordingly.

Note: You do not have to create the design in JMP in order to assess it using the Evaluate Design platform.

Some of the diagnostic measures include the following:

• power ...

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JMP 11 Design of Experiments Guide

by SAS Institute

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.