book

JMP 10 Design of Experiments Guide

Name: JMP 10 Design of Experiments Guide
Author: SAS Institute
ISBN: 9781612902180

by SAS Institute

March 2012

Intermediate to advanced

346 pages

8h 17m

English

SAS Institute

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Cover Page
Title Page
Copyright Page
Contents
Learn About JMP
Book Conventions
JMP DocumentationJMP Documentation SuiteJMP HelpJMP Books by UsersJMPer CableAdditional Resources for Learning JMPTutorialsThe JMP Starter WindowSample Data TablesLearn about Statistical and JSL TermsLearn JMP Tips and TricksTooltipsAccess Resources on the Web
Introduction to Designing Experiments
About Designing Experiments
My First ExperimentThe SituationStep 1: Design the ExperimentStep 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
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 DesignsScreening 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 with Two Hard-to-Change Factors: Split PlotTechnical Discussion

Building Custom Designs
Creating a Custom Design
Enter Responses and Factors into the Custom DesignerDescribe the ModelSpecifying Alias TermsSelect the Number of RunsUnderstanding Design EvaluationSpecify 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 Matrix: Viewing the Number of Rows in the Moments Matrix and the Design Matrix (X) in the LogOptimality CriterionNumber of Starts: Changing the Number of Random StartsSphere Radius: Constraining a Design to a HypersphereDisallowed Combinations: Accounting for Factor Level RestrictionsAdvanced Options for the Custom DesignerAssigning 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
Screening Designs
Screening Design Examples
Using Two Continuous Factors and One Categorical FactorUsing Five Continuous FactorsCreating a Screening DesignEnter ResponsesEnter FactorsChoose a DesignDisplay and Modify a DesignSpecify Output OptionsView the Design TableCreate a Plackett-Burman designAnalysis of Screening DataUsing the Screening Analysis PlatformUsing the Fit Model Platform
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 DesignSpecify 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 DesignThe 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 DesignCreating 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 DesignsBorehole 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
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
Launching the Evaluate Design Platform
The Evaluate Design ReportExamplesAssessing 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 MeanSample Size and Power Animation for One MeanTwo-Sample Meansk-Sample MeansOne Sample Standard DeviationOne Sample Standard Deviation ExampleOne-Sample and Two-Sample ProportionsOne Sample ProportionTwo Sample ProportionsCounts per UnitCounts per Unit ExampleSigma Quality LevelSigma Quality Level ExampleNumber of Defects Computation ExampleReliability Test Plan and DemonstrationReliability Test PlanReliability Demonstration
References
Index

Content preview from JMP 10 Design of Experiments Guide

Chapter 7

Full Factorial Designs

A full factorial design contains all possible combinations of a set of factors. This is the most fool proof design approach, but it is also the most costly in experimental resources. The full factorial designer supports both continuous factors and categorical factors with up to nine levels.

In full factorial designs, you perform an experimental run at every combination of the factor levels. The sample size is the product of the numbers of levels of the factors. For example, a factorial experiment with a two-level factor, a three-level factor, and a four-level factor has 2 x 3 x 4 = 24 runs.

Factorial designs ...

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JMP 10 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.