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

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

by SAS Institute
Released February 2017
Publisher(s): SAS Institute
ISBN: 9781629609454

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Book description

The JMP 13 Design of Experiments Guide covers classic DOE designs (for example, full factorial, response surface, and mixture designs). Read about more flexible custom designs, which you generate to fit your particular experimental situation. And discover JMP’s definitive screening designs, an efficient way to identify important factor interactions using fewer runs than required by traditional designs. The book also provides guidance on determining an appropriate sample size for your study.

Table of contents

  1. Contents
  2. Learn about JMP
    1. Documentation and Additional Resources
    2. Formatting Conventions
    3. JMP Documentation
      1. JMP Documentation Library
      2. JMP Help
    4. Additional Resources for Learning JMP
      1. Tutorials
      2. Sample Data Tables
      3. Learn about Statistical and JSL Terms
      4. Learn JMP Tips and Tricks
      5. Tooltips
      6. JMP User Community
      7. JMPer Cable
      8. JMP Books by Users
      9. The JMP Starter Window
    5. Technical Support
  3. Introduction to DOE
    1. Overview of Design of Experiment Platforms
  4. Starting Out with DOE
    1. Example and Key Concepts
    2. Overview of Experimental Design and the DOE Workflow
    3. The Coffee Strength Experiment
      1. Define the Study and Goals
      2. Create the Design
        1. Define Responses and Factors
        2. Specify the Model
        3. Steps to Duplicate Results (Optional)
        4. Generate the Design
        5. Evaluate the Design
        6. Make the Table
      3. Run the Experiment
      4. Analyze the Data
    4. The DOE Workflow: Describe, Specify, Design
      1. Define Responses and Factors
      2. Specify the Model
      3. Generate the Design
      4. Evaluate the Design
      5. Make the Table
    5. Principles and Guidelines for Experimental Design
      1. Effect Hierarchy
      2. Effect Heredity
      3. Effect Sparsity
      4. Center Points, Replicate Runs, and Testing
  5. Custom Designs
    1. Construct Designs That Meet Your Needs
    2. Overview of Custom Design
    3. Example of a Custom Design
      1. Create the Design
        1. Responses
        2. Factors
        3. Model
        4. Alias Terms
        5. Duplicate Results (Optional)
        6. Design Generation
        7. Design
        8. Design Evaluation
        9. Output Options
      2. Analyze the Data
        1. Interpret the Full Model Results
        2. Reduce the Model
        3. Interpret the Reduced Model Results
        4. Optimize Factor Settings
        5. Lock a Factor Level
        6. Profiler with Rater
        7. Summary
    4. Custom Design Window
      1. Responses
        1. Response Limits Column Property
      2. Factors
        1. Factors Outline
        2. Factor Types
        3. Changes and Random Blocks
        4. Factor Column Properties
      3. Define Factor Constraints
        1. Specify Linear Constraints
        2. Use Disallowed Combinations Filter
        3. Use Disallowed Combinations Script
      4. Model
      5. Alias Terms
      6. Design Generation
      7. Design
      8. Design Evaluation
      9. Output Options
    5. Custom Design Options
      1. Description of Options
      2. Simulate Responses
      3. Save X Matrix
      4. Number of Starts
      5. Design Search Time
      6. Set Delta for Power
    6. Technical Details
      1. Designs with Randomization Restrictions
        1. Random Block Designs
        2. Split-Plot Designs
        3. Split-Split-Plot Designs
        4. Two-Way Split-Plot Designs
      2. Covariates with Hard-to-Change Levels
      3. Numbers of Whole Plots and Subplots
      4. Optimality Criteria
        1. D-Optimality
        2. Bayesian D-Optimality
        3. I-Optimality
        4. Bayesian I-Optimality
        5. Alias Optimality
      5. D-Efficiency
      6. Coordinate-Exchange Algorithm
  6. Examples of Custom Designs
    1. Perform Experiments That Meet Your Needs
    2. Screening Experiments
      1. Design That Estimates Main Effects Only
      2. Design That Estimates All Two-Factor Interactions
      3. Design That Avoids Aliasing of Main Effects and Two-Factor Interactions
      4. Supersaturated Screening Designs
        1. Generate a Supersaturated Design
        2. Analyze a Supersaturated Design Using the Screening Platform
        3. Analyze a Supersaturated Design Using Stepwise Regression
      5. Design for Fixed Blocks
    3. Response Surface Experiments
      1. Response Surface Design
        1. Construct a Response Surface Design
        2. Analyze the Experimental Results
      2. Response Surface Design with Flexible Blocking
      3. Comparison of a D-Optimal and an I-Optimal Response Surface Design
        1. I-Optimal Design
        2. D-Optimal Design
      4. Response Surface Design With Constraints and Categorical Factor
    4. Mixture Experiments
      1. Mixture Design with Nonmixture Factors
      2. Mixture of Mixtures Design
    5. Experiments with Covariates
      1. Design with Fixed Covariates
      2. Design with Hard-to-Change Covariates
      3. Design with a Linear Time Trend
    6. Experiments with Randomization Restrictions
      1. Split-Plot Experiment
      2. Two-Way Split-Plot Experiment
  7. Augment Designs
    2. Example of Augment Design
      1. Analyze the Augmented Design
    3. Augment Design Launch Window
    4. Augment Design Window
      1. Factors
      2. Define Factor Constraints
        1. Specify Linear Constraints
        2. Use Disallowed Combinations Filter
        3. Use Disallowed Combinations Script
      3. Augmentation Choices
    5. Augment Design Platform Options
    6. Additional Examples of Augmentation Choices
      1. Replicate a Design
      2. Add Center Points to a Design
      3. Create a Foldover Design
      4. Add Axial Points to a Design
      5. Space Filling Augmentation
    7. Statistical Details
  8. Definitive Screening Designs
    2. Overview of Definitive Screening Design
    3. Examples of Definitive Screening Designs
      1. Definitive Screening Design
        1. Create the Design
      2. Comparison with a Fractional Factorial Design
      3. Definitive Screening Design with Blocking
        1. Create the Design
        2. Analyze the Experimental Data
      4. Comparison of a Definitive Screening Design with a Plackett-Burman Design
    4. Definitive Screening Design Window
      1. Responses
        1. Response Limits Column Property
      2. Factors
        1. Factor Types
        2. Factor Column Properties
      3. Design Options
        1. Blocking in Definitive Screening Designs
      4. Design
      5. Design Evaluation
      6. Output Options
    5. Definitive Screening Design Options
      1. Simulate Responses
    6. Technical Details
      1. Structure of Definitive Screening Designs
        1. Conference Matrices and the Number of Runs
        2. Extra Runs
        3. Definitive Screening Designs for Four or Fewer Factors
      2. Analysis of Experimental Data
        1. Two-Way Interactions
        2. Forward Stepwise Regression or All Possible Subsets Regression
  9. The Fit Definitive Screening Platform
    1. Analyze Data from Definitive Screening Experiments
    2. Overview of the Fit Definitive Screening Platform
      1. Identification of Active Effects in DSDs
      2. Effective Model Selection for DSDs
    3. Example of the Fit Definitive Screening Platform
      1. Fit the Model
      2. Examine Results
      3. Reduce the Model
    4. Launch the Fit Definitive Screening Platform
    5. Fit Definitive Screening Report
      1. Stage 1 - Main Effect Estimates
      2. Stage 2 - Even Order Effect Estimates
      3. Combined Model Parameter Estimates
      4. Main Effects Plot
      5. Prediction Profiler
    6. Fit Definitive Screening Platform Options
    7. Technical Details
      1. The Effective Model Selection for DSDs Algorithm
        1. Decomposition of Response
        2. Stage 1 Methodology
        3. Stage 2 Methodology
  10. Screening Designs
    2. Overview of Screening Designs
      1. Underlying Principles
      2. Analysis of Screening Design Results
    3. Examples of Screening Designs
      1. Compare a Fractional Factorial Design and a Main Effects Screening Design
        1. Constructing a Standard Screening Design
        2. Specify the Response
        3. Specify Factors
        4. Constructing a Main Effects Screening Design
      2. Main Effects Screening Design where No Standard Design Exists
    4. Screening Design Window
      1. Responses
        1. Response Limits Column Property
      2. Factors
        1. Factors Outline
        2. Factor Column Properties
      3. Choose Screening Type
      4. Choose from a List of Fractional Factorial Designs
      5. Design Type
        1. Two-Level Full Factorial
        2. Two-Level Regular Fractional Factorial
        3. Plackett-Burman Designs
        4. Mixed-Level Designs
        5. Cotter Designs
      6. Resolution as a Measure of Confounding
      7. Display and Modify Design
        1. Change Generating Rules
      8. Main Effects Screening Designs
        1. Chi-Square Efficiency
      9. Design Generation
      10. Design
      11. Design Evaluation
      12. Output Options
      13. Make Table
    5. Screening Design Options
    6. Additional Examples of Screening Designs
      1. Modify Generating Rules in a Fractional Factorial Design
        1. Create the Standard Fractional Factorial Design
        2. Change the Generating Rules to Obtain a Different Fraction
        3. Analyze the Results
      2. Plackett-Burman Design
  11. The Fit Two Level Screening Platform
    1. Analyze Data from Screening Experiments
    2. Overview of the Fit Two Level Screening Platform
    3. An Example Comparing Fit Two Level Screening and Fit Model
    4. Launch the Fit Two Level Screening Platform
    5. The Screening Report
      1. Contrasts
      2. Half Normal Plot
      3. Using the Fit Model Platform
        1. The Actual-by-Predicted Plot
        2. The Scaled Estimates Report
        3. A Power Analysis
    6. Additional Fit Two Level Screening Analysis Examples
      1. Analyzing a Plackett-Burman Design
      2. Analyzing a Supersaturated Design
    7. Technical Details
      1. Order of Effect Entry
      2. Fit Two Level Screening as an Orthogonal Rotation
      3. Lenth’s Pseudo-Standard Error
      4. Lenth t-Ratios
  12. Response Surface Designs
    2. Overview of Response Surface Designs
    3. Example of a Response Surface Design
      1. Construct a Box-Behnken Design
      2. Analyze the Experimental Data
      3. Explore Optimal Settings
    4. Response Surface Design Window
      1. Responses
        1. Response Limits Column Property
      2. Factors
        1. Factor Column Properties
      3. Choose a Design
        1. Box-Behnken Designs
        2. Central Composite Designs
      4. Specify Output Options
      5. Make Table
    5. Response Surface Design Options
  13. Full Factorial Designs
    2. Overview of Full Factorial Design
    3. Example of a Full Factorial Design
      1. Construct the Design
      2. Analyze the Experimental Data
        1. Analysis Using Screening Platform
        2. Analysis Using Stepwise Regression
        3. Optimal Settings Using the Prediction Profiler
    4. Full Factorial Design Window
      1. Responses
        1. Response Limits Column Property
      2. Factors
        1. Factors Outline
        2. Factor Column Properties
      3. Select Output Options
        1. Run Order
        2. Center Points and Replicates
      4. Make Table
        1. Design Table Scripts
        2. Pattern Column
    5. Full Factorial Design Options
  14. Mixture Designs
    2. Overview of Mixture Designs
    3. Mixture Design Window
      1. Responses
        1. Response Limits Column Property
      2. Factors
        1. Factors List
        2. Linear Constraints
      3. Ternary Plot Overview
    4. Examples of Mixture Design Types
      1. Optimal Mixture Design
      2. Simplex Centroid Design
        1. Creating the Design
      3. Simplex Lattice Design
      4. Extreme Vertices Design
        1. An Extreme Vertices Example with Range Constraints
        2. An Extreme Vertices Example with Linear Constraints
        3. Statistical Details for Extreme Vertices Method
      5. ABCD Design
      6. Space Filling Design
        1. Space Filling Example
        2. Statistical Details for Mixture Space Filling Designs
        3. FFF Optimality Criterion
        4. Set Average Cluster Size
        5. Linear Constraints
    5. Fitting Mixture Designs
      1. Whole Model Tests and Analysis of Variance Reports
      2. Understanding Response Surface Reports
    6. A Chemical Mixture Example
      1. Create the Design
      2. Analyze the Mixture Model
      3. The Prediction Profiler
      4. The Mixture Profiler
  15. Taguchi Designs
    2. Overview of Taguchi Designs
    3. Example of a Taguchi Design
    4. Taguchi Design Window
      1. Responses
      2. Factors
      3. Choose Inner and Outer Array Designs
      4. Display Coded Design
      5. Make the Design Table
  16. Evaluate Designs
    1. Explore Properties of Your Design
    2. Overview of Evaluate Design
    3. Example of Evaluate Design
      1. Assessing the Impact of Lost Runs
        1. Construct the Intended and Actual Designs
        2. Comparison of Intended and Actual Designs
      2. Evaluating Power Relative to a Specified Model
    4. Evaluate Design Launch Window
    5. Evaluate Design Window
      1. Factors
      2. Model
      3. Alias Terms
      4. Design
      5. Design Evaluation
      6. Power Analysis
        1. Power Analysis Overview
        2. Power Analysis Details
        3. Power Analysis for Coffee Experiment
      7. Prediction Variance Profile
      8. Fraction of Design Space Plot
      9. Prediction Variance Surface
      10. Estimation Efficiency
        1. Fractional Increase in CI Length
        2. Relative Std Error of Estimate
      11. Alias Matrix
        1. Alias Matrix Examples
      12. Color Map on Correlations
        1. Color Map Example
      13. Design Diagnostics
        1. Notation
        2. D Efficiency
        3. G Efficiency
        4. A Efficiency
        5. Average Variance of Prediction
        6. Design Creation Time
    6. Evaluate Design Options
  17. Compare Designs
    1. Compare and Evaluate Designs Simultaneously
    2. Overview of Comparing Designs
    3. Examples of Comparing Designs
      1. Designs of Same Run Size
        1. Comparison in Terms of Main Effects Only
      2. Designs of Different Run Sizes
      3. Split Plot Designs with Different Numbers of Whole Plots
    4. Compare Designs Launch Window
    5. Compare Designs Window: Specify Model and Alias Terms
      1. Reference Design
      2. Factors
      3. Model
      4. Alias Terms
    6. Compare Designs Window: Design Evaluation
      1. Power Analysis
        1. Power Analysis Report
        2. Power Plot
        3. Power versus Sample Size
      2. Prediction Variance Profile
      3. Fraction of Design Space Plot
      4. Relative Estimation Efficiency
        1. Relative Estimation Efficiency
        2. Relative Standard Error of Estimates
      5. Alias Matrix Summary
        1. Alias Matrix
        2. Example of Calculation of Alias Matrix Summary Values
      6. Absolute Correlations
        1. Absolute Correlations Table
        2. Color Map on Correlations
        3. Absolute Correlations and Color Map on Correlations Example
      7. Design Diagnostics
        1. Efficiency and Additional Run Size
        2. Relative Efficiency Measures
    7. Compare Designs Options
  18. Prospective Sample Size and Power
    2. Launching the Sample Size and Power Platform
    3. One-Sample and Two-Sample Means
      1. Single-Sample Mean
        1. Power versus Sample Size Plot
        2. Power versus Difference Plot
      2. Sample Size and Power Animation for One Mean
      3. Two-Sample Means
        1. Plot of Power by Sample Size
    4. k-Sample Means
    5. One Sample Standard Deviation
      1. One Sample Standard Deviation Example
    6. One-Sample and Two-Sample Proportions
      1. Actual Test Size
      2. One Sample Proportion
        1. One-Sample Proportion Window Specifications
      3. Two Sample Proportions
        1. Two Sample Proportion Window Specifications
    7. Counts per Unit
      1. Counts per Unit Example
    8. Sigma Quality Level
      1. Sigma Quality Level Example
      2. Number of Defects Computation Example
    9. Reliability Test Plan and Demonstration
      1. Reliability Test Plan
        1. Example
      2. Reliability Demonstration
        1. Example
  19. Discrete Choice Designs
    1. Create a Design for Selecting Preferred Product Profiles
    2. Overview of Choice Designs
      1. Choice Design Terminology
      2. Bayesian D-Optimality
    3. Example of a Choice Design
    4. Example of a Choice Design with Analysis
      1. Create a Choice Design for a Pilot Study
        1. Define Factors and Levels
        2. Create the Design
      2. Analyze the Pilot Study Data
      3. Design the Final Choice Experiment Using Prior Information
      4. Run the Design and Analyze the Results
        1. Determine Significant Attributes
        2. Find Unit Cost and Trade Off Costs
    5. Choice Design Window
      1. Attributes
        1. Attribute Column Properties
      2. Model
        1. DOE Model Controls
        2. Prior Specification
      3. Design Generation
      4. Design
        1. Output Options
      5. Make Table
    6. Choice Design Options
    7. Technical Details
      1. Bayesian D-Optimality and Design Construction
      2. Utility-Neutral and Local D-Optimal Designs
  20. MaxDiff Design
    1. Create a Design for Selecting Best and Worst Items
    2. MaxDiff Design Platform Overview
    3. Example of a MaxDiff Design
    4. MaxDiff Design Launch Window
    5. MaxDiff Window
      1. Design Options Outline
      2. Design Outline
      3. Make Table
    6. MaxDiff Options
  21. Covering Arrays
    1. Detecting Component Interaction Failures
    2. Overview of Covering Arrays
    3. Example of a Covering Array with No Factor Level Restrictions
      1. Create the Design
      2. Analyze the Experimental Data
    4. Example of a Covering Array with Factor Level Restrictions
      1. Create the Design
        1. Load Factors
        2. Restrict Factor Level Combinations
        3. Specify Disallowed Combinations Using the Filter
        4. Specify Disallowed Combinations Using a Script
        5. Construct the Design Table
      2. Analyze the Experimental Data
    5. Covering Array Window
      1. Factors
        1. Factors Table
        2. Editing the Factors Table
        3. Factor Column Properties
      2. Restrict Factor Level Combinations
        1. Use Disallowed Combinations Filter
        2. Use Disallowed Combinations Script
      3. Design
        1. Unsatisfiable Constraints
      4. Metrics
      5. Output Options
      6. The Covering Array Data Table
        1. Analysis Script
    6. Covering Array Options
    7. Technical Details
      1. Algorithm for Optimize
      2. Formulas for Metrics
        1. Unconstrained Design
        2. Constrained Design
  22. Space-Filling Designs
    2. Overview of Space-Filling Designs
    3. Space Filling Design Window
      1. Responses
        1. Response Limits Column Property
      2. Factors
        1. Factors Outline
        2. Factor Types
        3. Factor Column Properties
      3. Define Factor Constraints
        1. Specify Linear Constraints
        2. Use Disallowed Combinations Filter
        3. Use Disallowed Combinations Script
      4. Space Filling Design Methods
      5. Design
      6. Design Diagnostics
      7. Design Table
    4. Space Filling Design Options
    5. Sphere-Packing Designs
      1. Creating a Sphere-Packing Design
      2. Visualizing the Sphere-Packing Design
    6. Latin Hypercube Designs
      1. Creating a Latin Hypercube Design
      2. Visualizing the Latin Hypercube Design
    7. Uniform Designs
    8. Comparing Sphere-Packing, Latin Hypercube, and Uniform Methods
    9. Minimum Potential Designs
    10. Maximum Entropy Designs
    11. Gaussian Process IMSE Optimal Designs
    12. Fast Flexible Filling Designs
      1. FFF Optimality Criterion
        1. Categorical Factors
      2. Set Average Cluster Size
      3. Constraints
      4. Creating and Viewing a Constrained Fast Flexible Filling Design
    13. Example of a Sphere-Packing Design
      1. Create the Sphere-Packing Design for the Borehole Data
      2. Guidelines for the Analysis of Deterministic Data
        1. Analysis of the Borehole Sphere-Packing Design
  23. Accelerated Life Test Designs
    1. Designing Experiments for Accelerated Life Tests
    2. Overview of Accelerated Life Test Designs
    3. Example of an Accelerated Life Test Design
      1. Obtain Prior Estimates
      2. Enter Basic Specifications
      3. Enter Prior Information and Remaining Specifications
      4. Create the Design
    4. Example of Augmenting an Accelerated Life Test Design
    5. Accelerated Life Test Plan Window
      1. Specify the Design Structure
      2. Specify Acceleration Factors
      3. Specify Design Details
      4. Review Balanced Design Diagnostics and Update Specifications
      5. Create and Assess the Optimal Design
      6. Update the Design and Create Design Tables
    6. Platform Options
    7. Statistical Details
      1. Lognormal
      2. Weibull
  24. Nonlinear Designs
    2. Overview of Nonlinear Designs
    3. Examples of Nonlinear Designs
      1. Create a Nonlinear Design with No Prior Data
        1. Create the Design
        2. Explore the Design
        3. Analyze the Results
      2. Augment a Design Using Prior Data
        1. Obtain Prior Parameter Estimates
        2. Augment the Design
      3. Create a Design for a Binomial Response
        1. Create the Design
        2. View the Design
    4. Nonlinear Design Launch Window
    5. Nonlinear Design Window
      1. Factors
      2. Parameters
      3. Design Generation
      4. Design
      5. Make Table or Augment Table
    6. Nonlinear Design Options
    7. Statistical Details
      1. Nonlinear Models
      2. Radial-Spherical Integration of the Optimality Criterion
      3. Finding the Optimal Design
  25. Column Properties
    1. Understanding Column Properties Assigned by DOE
    2. Adding and Viewing Column Properties
    3. Response Limits
      1. Response Limits Example
      2. Editing Response Limits
    4. Design Role
      1. Design Role Example
    5. Coding
      1. Low and High Values
      2. Coding Column Property and Center Polynomials
      3. Coding Example
      4. Assigning Coding
    6. Mixture
      1. Mixture Example
    7. Factor Changes
      1. Factor Changes Example
    8. Value Ordering
      1. Value Ordering Example
      2. Assigning Value Ordering
    9. Value Labels
      1. Value Labels Example
    10. RunsPerBlock
      1. RunsPerBlock Example
    11. ConstraintState
      1. ConstraintState Example
  26. Technical Details
    2. The Alias Matrix
      1. Designs with Hard or Very Hard Factor Changes
      2. Designs with If Possible Effects
    3. Power Calculations
      1. Power for a Single Parameter
      2. Power for a Categorical Effect
    4. Relative Prediction Variance
  27. References
  28. Index
    1. Design of Experiments Guide

Product information

  • Title: JMP 13 Design of Experiments Guide, Second Edition, 2nd Edition
  • Author(s): SAS Institute
  • Release date: February 2017
  • Publisher(s): SAS Institute
  • ISBN: 9781629609454