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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

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

Product information

• Title: JMP 13 Design of Experiments Guide
• Author(s): SAS Institute
• Release date: September 2016
• Publisher(s): SAS Institute
• ISBN: 9781629605623

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Article Contents

• < Previous

Introduction to Design of Experiments with JMP Examples

• Article contents
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• Supplementary Data

S. E. Lazic, Introduction to Design of Experiments with JMP Examples, Journal of the Royal Statistical Society Series A: Statistics in Society , Volume 173, Issue 3, July 2010, Pages 692–693, https://doi.org/10.1111/j.1467-985X.2010.00646_5.x

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This book is written for practising engineers and researchers, and thus has a very applied focus; it would perhaps be less suitable for a course textbook, since problems and exercises are not included. All the examples are from situations that would be found in an industrial setting, especially manufacturing and production.

The book does a fair job of covering basic concepts and ideas and illustrates the results by using JMP software (from the SAS Institute). The book is not a step-by-step manual showing how to use JMP for the design of experiments, as there is a separation between ‘what to do conceptually’ and ‘how actually to do it in JMP’. Only in the final chapter (which is more like an appendix) are details given regarding implementation.

Regarding the structure of the book, one could question the logic of putting the chapter ‘Statistical concepts for designed experiments’ (Chapter 5) after ‘A three-factor designed experiment’ (Chapter 3) and ‘Four-factor full-factorial experiments’ (Chapter 4). Concepts such as populations versus samples, degrees of freedom and interactions should arguably be introduced before a full factorial analysis.

The style is very ‘choppy’: almost as if a PowerPoint presentation had been made into a book. Many pages contained excessive blank space, partly because of the size of the headings and subheadings, and often there is very little text under either. I was left with the impression that the authors started with a skeleton of topics that they wanted to discuss but did not quite flesh them out. Often there are several consecutive paragraphs that are only a sentence in length, and all relating to the same topic; these could have been combined into a single paragraph and would have made the ideas flow together more smoothly. It is thus not very dense with information and, if the book were to be reformatted in the style of CRC Press or Springer for example, it would be reduced in size by half. There were also many errors in referencing equations (more than expected given that it is the third edition), and values in the figures did not always correspond to values in the text or figure captions.

The book does describe concepts in an intuitive manner, and the chapter on optimal designs (Chapter 11) is particularly good in this regard. However, the topics were discussed somewhat superficially, and greater detail in many parts of the book would have been welcome.

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• Design of Experiments (DOE) Made Easy with JMP: A Comprehensive Guide for University Students

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Understanding the Essence of the Design of Experiments (DOE)

Why is doe important, 1. intuitive design planning, 2. efficient data collection, 3. robust statistical analysis, 4. visualization and interpretation, 5. interaction effects, 6. user community and resources, step-by-step guide to conducting doe in jmp, tips for success with doe and jmp.

Design of Experiments (DOE) is a powerful statistical technique used in various fields, including manufacturing, pharmaceuticals, and research. It allows you to systematically plan and conduct experiments to gather meaningful data, analyze it efficiently, and make informed decisions. When it comes to DOE, JMP stands out as a top-notch software tool known for its comprehensive capabilities in designing and analyzing experiments. In this blog, we will delve into the world of DOE and show university students how to leverage JMP to write their design of experiments assignment .

Design of Experiments is a structured approach to experimentation that aims to optimize processes, improve product quality, and gain valuable insights. It is rooted in statistical principles and involves planning, conducting, and analyzing experiments systematically and efficiently.

DOE is crucial because it allows for efficient experimentation, enabling organizations to optimize processes, reduce costs, and improve product quality. By systematically varying and controlling factors, DOE helps identify the key drivers of outcomes and minimizes the need for extensive trial and error, ultimately leading to informed decision-making. DOE offers numerous advantages in various industries:

• 1. Efficiency: DOE helps you achieve meaningful results with fewer experiments, saving time and resources.
• 2. Accuracy: By controlling variables and minimizing external influences, you can obtain more precise and reliable data.
• 3. Cost Reduction: It enables you to identify and eliminate non-essential factors, reducing unnecessary expenses.
• 4. Optimization: DOE helps you find the optimal settings or conditions that lead to the desired outcomes.
• 5. Innovation: It encourages creativity and innovation by systematically exploring different variables and their interactions.

Now, let's explore how JMP can be your go-to tool for mastering DOE.

The Power of JMP in the Design of Experiments

JMP is widely recognized for its user-friendly interface and powerful statistical tools, making it an ideal choice for students and professionals alike. Here's how JMP can assist you in your DOE assignments and research:

JMP simplifies the process of planning your experiments. It guides you through the critical steps of DOE, including:

• Factor Selection: Choose the factors (independent variables) that you want to investigate in your experiment.
• Response Variables: Specify the outcomes or responses you want to measure.
• Experimental Design: Select the type of design you need, whether it's a full factorial, fractional factorial, or response surface design.
• Randomization: Ensure that the experiment is conducted in a randomized order to minimize bias.
• Replication: Determine the number of times each experiment should be replicated for reliable results.

With JMP, data collection becomes more organized and efficient. You can use it to create data collection forms, input data directly, and store all your experiment data in one place. JMP also offers real-time data visualization, helping you monitor your experiments as they progress.

Once you have collected your data, JMP provides a wide range of statistical tools to analyze it effectively. Some of the key analyses you can perform include:

• Analysis of Variance (ANOVA): This helps you determine if there are statistically significant differences between groups.
• Regression Analysis: Explore relationships between variables and build predictive models.
• Optimization: Use JMP's optimization tools to find the optimal settings for your variables.
• Response Surface Analysis: Understand complex relationships between multiple factors and responses.

JMP excels in data visualization, making it easier to interpret your results. You can create various plots, charts, and graphs to visualize the effects of different factors on your responses. Visualizations help you communicate your findings effectively and make informed decisions.

One of the powerful features of JMP is its ability to uncover interaction effects between factors. Interaction effects occur when the combined influence of two or more factors is different from what you'd expect based on their individual effects. JMP can help you identify and visualize these interactions, providing valuable insights into your experiments.

JMP has a thriving user community and provides extensive resources for learning and troubleshooting. You can access tutorials, documentation, and forums where you can seek help and share your knowledge with others.

Now, let's walk through a step-by-step guide on how to conduct a Design of Experiments using JMP:

Clearly state the problem you want to address through your experiment. Determine the factors and responses you need to consider.

Open JMP, and start a new project. Give it a descriptive name related to your experiment.

In JMP, organize your data in a table format. Define columns for factors, responses, and any other relevant information.

Step 4: Design Your Experiment

Use JMP's DOE capabilities to create your experimental design. Select the appropriate design type, specify factors, and set the number of runs or replicates.

Step 5: Collect Data

Record your experimental data in the JMP data table. You can enter data manually or import it from external sources.

Step 6: Analyze Data

Perform statistical analyses on your data using JMP's built-in tools. Look for significant effects and interactions between factors.

Step 7: Visualize Results

Create visualizations like scatter plots, contour plots, and response surface plots to better understand your data.

Step 8: Interpret and Draw Conclusions

Based on your analysis and visualizations, conclude the factors that impact your responses. Identify optimal settings if applicable.

Step 9: Report Your Findings

Present your findings in a clear and organized manner, including tables and graphs generated in JMP. Explain your results and their implications.

Step 10: Iterate and Refine

If necessary, refine your experiment and repeat the process to further optimize your results.

To excel in your DOE assignments and research with JMP, keep these tips in mind:

• 1. Practice Regularly: The more you work with JMP, the more proficient you'll become. Practice creating experiments and analyzing data to build your skills.
• 2. Seek Help and Resources: Don't hesitate to use JMP's documentation and seek assistance from the user community or your instructors if you encounter challenges.
• 3. Think Critically: Always think critically about the factors you select and the hypotheses you test. This will lead to more meaningful experiments and results.
• 4. Visualize Your Data: Visualizations can reveal insights that may not be apparent from raw data. Make use of JMP's visualization tools to explore your data thoroughly.
• 5. Collaborate: If you're working on a group project, consider collaborating with peers. Sharing insights and perspectives can lead to more robust experiments.

Design of Experiments (DOE) is a vital technique for optimizing processes, improving product quality, and gaining valuable insights in various industries. When it comes to DOE, JMP is an invaluable tool for university students looking to excel in their assignments and research. Its intuitive interface, powerful statistical tools, and robust data visualization capabilities make it a go-to choice for those venturing into the world of experimentation. By following the step-by-step guide and tips provided in this blog, you'll be well-equipped to tackle the design of experiments (DOE) assignments and make the most out of JMP's capabilities. Remember that practice and a deep understanding of your problem domain are key to becoming a proficient DOE practitioner. So, embrace the power of DOE and JMP, and embark on your journey to becoming a successful experimenter and analyst.

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