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