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JMP 12 Design of Experiments Guide

Book Description

JMP 12 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. Discover JMP’s definitive screening designs, an efficient way to identify important factor interactions using fewer runs than required by traditional designs. And read about creating designs that test systems where failures occur as a result of interactions among components or subsystems. 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. Discovering JMP
      3. Using JMP
      4. Basic Analysis
      5. Essential Graphing
      6. Profilers
      7. Design of Experiments Guide
      8. Fitting Linear Models
      9. Specialized Models
      10. Multivariate Methods
      11. Quality and Process Methods
      12. Reliability and Survival Methods
      13. Consumer Research
      14. Scripting Guide
      15. JSL Syntax Reference
      16. 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
  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. Steps to 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
    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. Save X Matrix
      2. Number of Starts
      3. Design Search Time
      4. 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. Generating a Supersaturated Design
        2. Analyzing a Supersaturated Design Using the Screening Platform
        3. Analyzing a Supersaturated Design Using Stepwise Regression
      5. Design for Fixed Blocks
    3. Response Surface Experiments
      1. Response Surface Design
        1. Constructing a Response Surface Design
        2. Analyzing 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. 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. Definitive Screening Designs
    1. Overview of Definitive Screening Design
    2. Examples of Definitive Screening Designs
      1. Definitive Screening Design
        1. Create the Design
        2. Analyze the Experimental Data
      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
    3. Definitive Screening Design Window
      1. Responses
        1. Response Limits Column Property
      2. Factors
        1. Factor Types
        2. Factor Column Properties
      3. Orthogonal Blocking Options
      4. Blocking in Definitive Screening Designs
        1. Add Blocks with Center Runs to Estimate Quadratic Effects
        2. Add Blocks without Extra Center Runs
      5. Design
      6. Design Evaluation
      7. Output Options
    4. Definitive Screening Design Options
    5. Technical Details
      1. Structure of Definitive Screening Designs
        1. Conference Matrices and the Number of Runs
        2. Definitive Screening Designs for Four or Fewer Factors
      2. Analysis of Experimental Data
  8. Screening Designs
    1. Overview of Screening Designs
    2. Screening Design Examples
      1. A Standard Design with Two Continuous Factors and One Categorical Factor
      2. A Standard Design for Five Continuous Factors
    3. Creating a Screening Design
      1. Enter Responses
        1. Specifying Goal Types and Lower and Upper Limits
        2. Understanding Importance Weights
      2. Enter Factors
        1. Types of Factors
      3. Choose Screening Type
      4. Choose from a List of Fractional Factorial Designs
        1. Design List
        2. Display and Modify Design
        3. Aliasing of Effects
        4. Look at the Confounding Pattern
        5. Understanding Design Codes
        6. Changing the Coded Design
      5. Design Generation
      6. Near-Orthogonal Designs
        1. Related Options
        2. Chi2 Efficiency
      7. Design Evaluation
      8. Specify Output Options
      9. View the Design Table
    4. Creating a Plackett-Burman Design
    5. Creating a Main Effects Screening Design
  9. The Screening Platform
    1. Analyze Data from Screening Experiments
    2. Overview of the Screening Platform
    3. An Example Comparing Screening and Fit Model
    4. Launch the 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 Screening Analysis Examples
      1. Analyzing a Plackett-Burman Design
      2. Analyzing a Supersaturated Design
    7. Technical Details
      1. Order of Effect Entry
      2. Screening as an Orthogonal Rotation
      3. Lenth’s Pseudo-Standard Error
  10. Response Surface Designs
    1. A Box-Behnken Design: The Tennis Ball Example
      1. The Prediction Profiler
      2. A Response Surface Plot (Contour Profiler)
      3. Geometry of a Box-Behnken Design
    2. Creating a Response Surface Design
      1. Enter Responses and Factors
      2. Choose a Design
        1. Box-Behnken Designs
        2. Central Composite Designs
        3. Specify Axial Value (Central Composite Designs Only)
      3. Specify Output Options
      4. View the Design Table
  11. Full Factorial Designs
    1. The Five-Factor Reactor Example
      1. Analyze the Reactor Data
    2. Creating a Factorial Design
      1. Enter Responses and Factors
      2. Select Output Options
      3. Make the Table
  12. Mixture Designs
    1. Mixture Design Types
    2. The Optimal Mixture Design
      1. Adding Effects to the Model
    3. The Simplex Centroid Design
      1. Creating the Design
      2. Simplex Centroid Design Examples
    4. The Simplex Lattice Design
    5. The Extreme Vertices Design
      1. Creating the Design
      2. An Extreme Vertices Example with Range Constraints
      3. An Extreme Vertices Example with Linear Constraints
      4. Extreme Vertices Method: How It Works
    6. The ABCD Design
    7. The Space Filling Design
      1. FFF Optimality Criterion
      2. Set Average Cluster Size
      3. Linear Constraints
      4. A Space Filling Example
      5. A Space Filling Example with a Linear Constraint
    8. Creating Ternary Plots
    9. Fitting Mixture Designs
      1. Whole Model Tests and Analysis of Variance Reports
      2. Understanding Response Surface Reports
    10. A Chemical Mixture Example
      1. Create the Design
      2. Analyze the Mixture Model
      3. The Prediction Profiler
      4. The Mixture Profiler
      5. A Ternary Plot of the Mixture Response Surface
  13. Covering Arrays
    1. Detecting Component Interaction Failures
    2. Overview of Covering Arrays
      1. Covering Arrays and Strength
    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
  14. Discrete Choice Designs
    1. Create a Choice Design with No Prior Information
    2. Create an Example Choice Experiment
    3. Create a Choice Design and Analyze the Data
      1. Create a Choice Experiment for a Pilot Study
      2. Analyze the Pilot Study Data
      3. Design a Choice Experiment Using Prior Information
      4. Administer the Survey and Analyze Results
        1. Initial Choice Platform Analysis
        2. Find Unit Cost and Trade Off Costs with the Profiler
    4. Choice Design Options
  15. Space-Filling Designs
    1. Overview of Space-Filling Designs
    2. 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
    3. Space Filling Design Options
    4. Sphere-Packing Designs
      1. Creating a Sphere-Packing Design
      2. Visualizing the Sphere-Packing Design
    5. Latin Hypercube Designs
      1. Creating a Latin Hypercube Design
      2. Visualizing the Latin Hypercube Design
    6. Uniform Designs
    7. Comparing Sphere-Packing, Latin Hypercube, and Uniform Methods
    8. Minimum Potential Designs
    9. Maximum Entropy Designs
    10. Gaussian Process IMSE Optimal Designs
    11. Fast Flexible Filling Designs
      1. FFF Optimality Criterion
      2. Set Average Cluster Size
      3. Constraints
      4. Creating and Viewing a Constrained Fast Flexible Filling Design
    12. Borehole Model: A Sphere-Packing Example
      1. Create the Sphere-Packing Design for the Borehole Data
      2. Guidelines for the Analysis of Deterministic Data
        1. Results of the Borehole Experiment
  16. Accelerated Life Test Designs
    1. Designing Experiments for Accelerated Life Tests
    2. Overview of Accelerated Life Test Designs
    3. Using the ALT Design Platform
    4. Platform Options
    5. Example
  17. Nonlinear Designs
    1. Examples of Nonlinear Designs
      1. Using Nonlinear Fit to Find Prior Parameter Estimates
      2. Creating a Nonlinear Design with No Prior Data
    2. Creating a Nonlinear Design
      1. Identify the Response and Factor Column with Formula
      2. Set Up Factors and Parameters in the Nonlinear Design Dialog
      3. Enter the Number of Runs and Preview the Design
      4. Make Table or Augment the Table
    3. Advanced Options for the Nonlinear Designer
  18. Taguchi Designs
    1. The Taguchi Design Approach
    2. Taguchi Design Example
      1. Analyze the Data
    3. Creating a Taguchi Design
      1. Detail the Response and Add Factors
      2. Choose Inner and Outer Array Designs
      3. Display Coded Design
      4. Make the Design Table
  19. 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
    7. Technical Details
      1. Power Calculations
        1. Power for a Single Parameter
        2. Power for a Categorical Effect
      2. Relative Prediction Variance
  20. Augmented Designs
    1. A D-Optimal Augmentation of the Reactor Example
      1. Analyze the Augmented Design
    2. Augmentation Choices
      1. Replicate a Design
      2. Add Center Points
      3. Creating a Foldover Design
      4. Adding Axial Points
      5. Space Filling
      6. Adding New Runs and Terms
    3. Define Factor Constraints
      1. Specify Linear Constraints
      2. Use Disallowed Combinations Filter
      3. Use Disallowed Combinations Script
    4. Special Augment Design Options
      1. Save the Design (X) Matrix
      2. Modify the Design Criterion (D- or I- Optimality)
      3. Select the Number of Random Starts
      4. Design Search Time
      5. Specify the Sphere Radius Value
  21. Prospective Sample Size and Power
    1. Launching the Sample Size and Power Platform
    2. 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
    3. k-Sample Means
    4. One Sample Standard Deviation
      1. One Sample Standard Deviation Example
    5. 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
    6. Counts per Unit
      1. Counts per Unit Example
    7. Sigma Quality Level
      1. Sigma Quality Level Example
      2. Number of Defects Computation Example
    8. Reliability Test Plan and Demonstration
      1. Reliability Test Plan
        1. Example
      2. Reliability Demonstration
        1. Example
  22. 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
  23. Technical Details
    1. The Model and Alias Matrices
    2. The Model Matrix
    3. The Alias Matrix
      1. Designs with Hard or Very Hard Factor Changes
      2. Designs with If Possible Effects
  24. References
  25. Index
    1. Design of Experiments Guide
    2. A
    3. B
    4. C
    5. D
    6. E
    7. F
    8. G
    9. H
    10. I
    11. J
    12. K
    13. L
    14. M
    15. N
    16. O
    17. P
    18. Q
    19. R
    20. S
    21. T
    22. U
    23. V
    24. W-Z