book

JMP 13 Design of Experiments Guide, Second Edition, 2nd Edition

Name: JMP 13 Design of Experiments Guide, Second Edition, 2nd Edition
Author: SAS Institute
ISBN: 9781629609454

by SAS Institute

February 2017

Intermediate to advanced

694 pages

17h 48m

English

SAS Institute

Read now

Unlock full access

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 WindowTechnical Support
Introduction to DOE
Overview of Design of Experiment Platforms
Starting Out with DOE
Example and Key Concepts
Overview of Experimental Design and the DOE Workflow
The Coffee Strength ExperimentDefine the Study and GoalsCreate the DesignDefine Responses and FactorsSpecify the ModelSteps to Duplicate Results (Optional)Generate the DesignEvaluate the DesignMake the TableRun the ExperimentAnalyze the DataThe DOE Workflow: Describe, Specify, DesignDefine Responses and FactorsSpecify the ModelGenerate the DesignEvaluate the DesignMake the TablePrinciples and Guidelines for Experimental DesignEffect HierarchyEffect HeredityEffect SparsityCenter Points, Replicate Runs, and Testing
Custom Designs
Construct Designs That Meet Your Needs
Overview of Custom Design
Example of a Custom DesignCreate the DesignResponsesFactorsModelAlias TermsDuplicate Results (Optional)Design GenerationDesignDesign EvaluationOutput OptionsAnalyze the DataInterpret the Full Model ResultsReduce the ModelInterpret the Reduced Model ResultsOptimize Factor SettingsLock a Factor LevelProfiler with RaterSummaryCustom Design WindowResponsesResponse Limits Column PropertyFactorsFactors OutlineFactor TypesChanges and Random BlocksFactor Column PropertiesDefine Factor ConstraintsSpecify Linear ConstraintsUse Disallowed Combinations FilterUse Disallowed Combinations ScriptModelAlias TermsDesign GenerationDesignDesign EvaluationOutput OptionsCustom Design OptionsDescription of OptionsSimulate ResponsesSave X MatrixNumber of StartsDesign Search TimeSet Delta for PowerTechnical DetailsDesigns with Randomization RestrictionsRandom Block DesignsSplit-Plot DesignsSplit-Split-Plot DesignsTwo-Way Split-Plot DesignsCovariates with Hard-to-Change LevelsNumbers of Whole Plots and SubplotsOptimality CriteriaD-OptimalityBayesian D-OptimalityI-OptimalityBayesian I-OptimalityAlias OptimalityD-EfficiencyCoordinate-Exchange Algorithm
Examples of Custom Designs
Perform Experiments That Meet Your Needs
Screening Experiments
Design That Estimates Main Effects OnlyDesign That Estimates All Two-Factor InteractionsDesign That Avoids Aliasing of Main Effects and Two-Factor InteractionsSupersaturated Screening DesignsGenerate a Supersaturated DesignAnalyze a Supersaturated Design Using the Screening PlatformAnalyze a Supersaturated Design Using Stepwise RegressionDesign for Fixed BlocksResponse Surface ExperimentsResponse Surface DesignConstruct a Response Surface DesignAnalyze the Experimental ResultsResponse Surface Design with Flexible BlockingComparison of a D-Optimal and an I-Optimal Response Surface DesignI-Optimal DesignD-Optimal DesignResponse Surface Design With Constraints and Categorical FactorMixture ExperimentsMixture Design with Nonmixture FactorsMixture of Mixtures DesignExperiments with CovariatesDesign with Fixed CovariatesDesign with Hard-to-Change CovariatesDesign with a Linear Time TrendExperiments with Randomization RestrictionsSplit-Plot ExperimentTwo-Way Split-Plot Experiment

Augment Designs
Example of Augment Design
Analyze the Augmented DesignAugment Design Launch WindowAugment Design WindowFactorsDefine Factor ConstraintsSpecify Linear ConstraintsUse Disallowed Combinations FilterUse Disallowed Combinations ScriptAugmentation ChoicesAugment Design Platform OptionsAdditional Examples of Augmentation ChoicesReplicate a DesignAdd Center Points to a DesignCreate a Foldover DesignAdd Axial Points to a DesignSpace Filling AugmentationStatistical Details
Definitive Screening Designs
Overview of Definitive Screening Design
Examples of Definitive Screening DesignsDefinitive Screening DesignCreate the DesignComparison with a Fractional Factorial DesignDefinitive Screening Design with BlockingCreate the DesignAnalyze the Experimental DataComparison of a Definitive Screening Design with a Plackett-Burman DesignDefinitive Screening Design WindowResponsesResponse Limits Column PropertyFactorsFactor TypesFactor Column PropertiesDesign OptionsBlocking in Definitive Screening DesignsDesignDesign EvaluationOutput OptionsDefinitive Screening Design OptionsSimulate ResponsesTechnical DetailsStructure of Definitive Screening DesignsConference Matrices and the Number of RunsExtra RunsDefinitive Screening Designs for Four or Fewer FactorsAnalysis of Experimental DataTwo-Way InteractionsForward Stepwise Regression or All Possible Subsets Regression
The Fit Definitive Screening Platform
Analyze Data from Definitive Screening Experiments
Overview of the Fit Definitive Screening Platform
Identification of Active Effects in DSDsEffective Model Selection for DSDsExample of the Fit Definitive Screening PlatformFit the ModelExamine ResultsReduce the ModelLaunch the Fit Definitive Screening PlatformFit Definitive Screening ReportStage 1 - Main Effect EstimatesStage 2 - Even Order Effect EstimatesCombined Model Parameter EstimatesMain Effects PlotPrediction ProfilerFit Definitive Screening Platform OptionsTechnical DetailsThe Effective Model Selection for DSDs AlgorithmDecomposition of ResponseStage 1 MethodologyStage 2 Methodology
Screening Designs
Overview of Screening Designs
Underlying PrinciplesAnalysis of Screening Design ResultsExamples of Screening DesignsCompare a Fractional Factorial Design and a Main Effects Screening DesignConstructing a Standard Screening DesignSpecify the ResponseSpecify FactorsConstructing a Main Effects Screening DesignMain Effects Screening Design where No Standard Design ExistsScreening Design WindowResponsesResponse Limits Column PropertyFactorsFactors OutlineFactor Column PropertiesChoose Screening TypeChoose from a List of Fractional Factorial DesignsDesign TypeTwo-Level Full FactorialTwo-Level Regular Fractional FactorialPlackett-Burman DesignsMixed-Level DesignsCotter DesignsResolution as a Measure of ConfoundingDisplay and Modify DesignChange Generating RulesMain Effects Screening DesignsChi-Square EfficiencyDesign GenerationDesignDesign EvaluationOutput OptionsMake TableScreening Design OptionsAdditional Examples of Screening DesignsModify Generating Rules in a Fractional Factorial DesignCreate the Standard Fractional Factorial DesignChange the Generating Rules to Obtain a Different FractionAnalyze the ResultsPlackett-Burman Design
The Fit Two Level Screening Platform
Analyze Data from Screening Experiments
Overview of the Fit Two Level Screening Platform
An Example Comparing Fit Two Level Screening and Fit ModelLaunch the Fit Two Level Screening PlatformThe Screening ReportContrastsHalf Normal PlotUsing the Fit Model PlatformThe Actual-by-Predicted PlotThe Scaled Estimates ReportA Power AnalysisAdditional Fit Two Level Screening Analysis ExamplesAnalyzing a Plackett-Burman DesignAnalyzing a Supersaturated DesignTechnical DetailsOrder of Effect EntryFit Two Level Screening as an Orthogonal RotationLenth’s Pseudo-Standard ErrorLenth t-Ratios
Response Surface Designs
Overview of Response Surface Designs
Example of a Response Surface DesignConstruct a Box-Behnken DesignAnalyze the Experimental DataExplore Optimal SettingsResponse Surface Design WindowResponsesResponse Limits Column PropertyFactorsFactor Column PropertiesChoose a DesignBox-Behnken DesignsCentral Composite DesignsSpecify Output OptionsMake TableResponse Surface Design Options
Full Factorial Designs
Overview of Full Factorial Design
Example of a Full Factorial DesignConstruct the DesignAnalyze the Experimental DataAnalysis Using Screening PlatformAnalysis Using Stepwise RegressionOptimal Settings Using the Prediction ProfilerFull Factorial Design WindowResponsesResponse Limits Column PropertyFactorsFactors OutlineFactor Column PropertiesSelect Output OptionsRun OrderCenter Points and ReplicatesMake TableDesign Table ScriptsPattern ColumnFull Factorial Design Options
Mixture Designs
Overview of Mixture Designs
Mixture Design WindowResponsesResponse Limits Column PropertyFactorsFactors ListLinear ConstraintsTernary Plot OverviewExamples of Mixture Design TypesOptimal Mixture DesignSimplex Centroid DesignCreating the DesignSimplex Lattice DesignExtreme Vertices DesignAn Extreme Vertices Example with Range ConstraintsAn Extreme Vertices Example with Linear ConstraintsStatistical Details for Extreme Vertices MethodABCD DesignSpace Filling DesignSpace Filling ExampleStatistical Details for Mixture Space Filling DesignsFFF Optimality CriterionSet Average Cluster SizeLinear ConstraintsFitting Mixture DesignsWhole Model Tests and Analysis of Variance ReportsUnderstanding Response Surface ReportsA Chemical Mixture ExampleCreate the DesignAnalyze the Mixture ModelThe Prediction ProfilerThe Mixture Profiler
Taguchi Designs
Overview of Taguchi Designs
Example of a Taguchi DesignTaguchi Design WindowResponsesFactorsChoose Inner and Outer Array DesignsDisplay Coded DesignMake the Design Table
Evaluate Designs
Explore Properties of Your Design
Overview of Evaluate Design
Example of Evaluate DesignAssessing the Impact of Lost RunsConstruct the Intended and Actual DesignsComparison of Intended and Actual DesignsEvaluating Power Relative to a Specified Model
Evaluate Design Launch Window
Evaluate Design WindowFactorsModelAlias TermsDesignDesign EvaluationPower AnalysisPower Analysis OverviewPower Analysis DetailsPower Analysis for Coffee ExperimentPrediction Variance ProfileFraction of Design Space PlotPrediction Variance SurfaceEstimation EfficiencyFractional Increase in CI LengthRelative Std Error of EstimateAlias MatrixAlias Matrix ExamplesColor Map on CorrelationsColor Map ExampleDesign DiagnosticsNotationD EfficiencyG EfficiencyA EfficiencyAverage Variance of PredictionDesign Creation TimeEvaluate Design Options
Compare Designs
Compare and Evaluate Designs Simultaneously
Overview of Comparing Designs
Examples of Comparing DesignsDesigns of Same Run SizeComparison in Terms of Main Effects OnlyDesigns of Different Run SizesSplit Plot Designs with Different Numbers of Whole PlotsCompare Designs Launch WindowCompare Designs Window: Specify Model and Alias TermsReference DesignFactorsModelAlias TermsCompare Designs Window: Design EvaluationPower AnalysisPower Analysis ReportPower PlotPower versus Sample SizePrediction Variance ProfileFraction of Design Space PlotRelative Estimation EfficiencyRelative Estimation EfficiencyRelative Standard Error of EstimatesAlias Matrix SummaryAlias MatrixExample of Calculation of Alias Matrix Summary ValuesAbsolute CorrelationsAbsolute Correlations TableColor Map on CorrelationsAbsolute Correlations and Color Map on Correlations ExampleDesign DiagnosticsEfficiency and Additional Run SizeRelative Efficiency MeasuresCompare Designs Options
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 ProportionsActual Test SizeOne 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
Discrete Choice Designs
Create a Design for Selecting Preferred Product Profiles
Overview of Choice Designs
Choice Design TerminologyBayesian D-OptimalityExample of a Choice DesignExample of a Choice Design with AnalysisCreate a Choice Design for a Pilot StudyDefine Factors and LevelsCreate the DesignAnalyze the Pilot Study DataDesign the Final Choice Experiment Using Prior InformationRun the Design and Analyze the ResultsDetermine Significant AttributesFind Unit Cost and Trade Off CostsChoice Design WindowAttributesAttribute Column PropertiesModelDOE Model ControlsPrior SpecificationDesign GenerationDesignOutput OptionsMake TableChoice Design OptionsTechnical DetailsBayesian D-Optimality and Design ConstructionUtility-Neutral and Local D-Optimal Designs
MaxDiff Design
Create a Design for Selecting Best and Worst Items
MaxDiff Design Platform Overview
Example of a MaxDiff Design
MaxDiff Design Launch WindowMaxDiff WindowDesign Options OutlineDesign OutlineMake TableMaxDiff Options
Covering Arrays
Detecting Component Interaction Failures
Overview of Covering Arrays
Example of a Covering Array with No Factor Level RestrictionsCreate the DesignAnalyze the Experimental DataExample of a Covering Array with Factor Level RestrictionsCreate the DesignLoad FactorsRestrict Factor Level CombinationsSpecify Disallowed Combinations Using the FilterSpecify Disallowed Combinations Using a ScriptConstruct the Design TableAnalyze the Experimental DataCovering Array WindowFactorsFactors TableEditing the Factors TableFactor Column PropertiesRestrict Factor Level CombinationsUse Disallowed Combinations FilterUse Disallowed Combinations ScriptDesignUnsatisfiable ConstraintsMetricsOutput OptionsThe Covering Array Data TableAnalysis ScriptCovering Array OptionsTechnical DetailsAlgorithm for OptimizeFormulas for MetricsUnconstrained DesignConstrained Design
Space-Filling Designs
Overview of Space-Filling Designs
Space Filling Design WindowResponsesResponse Limits Column PropertyFactorsFactors OutlineFactor TypesFactor Column PropertiesDefine Factor ConstraintsSpecify Linear ConstraintsUse Disallowed Combinations FilterUse Disallowed Combinations ScriptSpace Filling Design MethodsDesignDesign DiagnosticsDesign TableSpace Filling Design OptionsSphere-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 DesignsFFF Optimality CriterionCategorical FactorsSet Average Cluster SizeConstraintsCreating and Viewing a Constrained Fast Flexible Filling DesignExample of a Sphere-Packing DesignCreate the Sphere-Packing Design for the Borehole DataGuidelines for the Analysis of Deterministic DataAnalysis of the Borehole Sphere-Packing Design
Accelerated Life Test Designs
Designing Experiments for Accelerated Life Tests
Overview of Accelerated Life Test Designs
Example of an Accelerated Life Test Design
Obtain Prior EstimatesEnter Basic SpecificationsEnter Prior Information and Remaining SpecificationsCreate the DesignExample of Augmenting an Accelerated Life Test DesignAccelerated Life Test Plan WindowSpecify the Design StructureSpecify Acceleration FactorsSpecify Design DetailsReview Balanced Design Diagnostics and Update SpecificationsCreate and Assess the Optimal DesignUpdate the Design and Create Design TablesPlatform OptionsStatistical DetailsLognormalWeibull
Nonlinear Designs
Overview of Nonlinear Designs
Examples of Nonlinear DesignsCreate a Nonlinear Design with No Prior DataCreate the DesignExplore the DesignAnalyze the ResultsAugment a Design Using Prior DataObtain Prior Parameter EstimatesAugment the DesignCreate a Design for a Binomial ResponseCreate the DesignView the DesignNonlinear Design Launch WindowNonlinear Design WindowFactorsParametersDesign GenerationDesignMake Table or Augment TableNonlinear Design OptionsStatistical DetailsNonlinear ModelsRadial-Spherical Integration of the Optimality CriterionFinding the Optimal Design
Column Properties
Understanding Column Properties Assigned by DOE
Adding and Viewing Column Properties
Response LimitsResponse Limits ExampleEditing Response LimitsDesign RoleDesign Role ExampleCodingLow and High ValuesCoding Column Property and Center PolynomialsCoding ExampleAssigning CodingMixtureMixture ExampleFactor ChangesFactor Changes ExampleValue OrderingValue Ordering ExampleAssigning Value OrderingValue LabelsValue Labels ExampleRunsPerBlockRunsPerBlock ExampleConstraintStateConstraintState Example
Technical Details
The Alias Matrix
Designs with Hard or Very Hard Factor ChangesDesigns with If Possible EffectsPower CalculationsPower for a Single ParameterPower for a Categorical EffectRelative Prediction Variance
References
Index
Design of Experiments Guide

Content preview from JMP 13 Design of Experiments Guide, Second Edition, 2nd Edition

Chapter 7

Definitive Screening Designs

Definitive screening designs work for factor screening if you have any combination of continuous or two-level categorical factors. These designs are particularly useful if you suspect active two-factor interactions, or you suspect that a plot of a continuous factor’s effect on the response might exhibit strong curvature.

Definitive screening designs are small designs. For six or more factors, there are only about twice as many runs as factors. Yet, they often conclusively identify which of several factors affect the response. In particular, they can detect and identify any factors causing strong nonlinear effects on the response.

Here are areas where definitive screening designs are superior to standard ...

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.

Read now

Unlock full access

More than 5,000 organizations count on O’Reilly

O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.

Julian F.

Head of Cybersecurity

I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.

Addison B.

Field Engineer

I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.

Amir M.

Data Platform Tech Lead

I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.

Mark W.

Embedded Software Engineer

Publisher Resources

ISBN: 9781629609454

Cloud Computing

Data Engineering

Data Science

AI & ML

Programming Languages

Software Architecture

IT/Ops

Security

Design

Business

Soft Skills

JMP 13 Design of Experiments Guide, Second Edition, 2nd Edition

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.