3rd Edition

DOE Simplified Practical Tools for Effective Experimentation, Third Edition

    268 Pages 85 B/W Illustrations
    by Productivity Press

    by Productivity Press

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    Offering a planned approach for determining cause and effect, DOE Simplified: Practical Tools for Effective Experimentation, Third Edition integrates the authors’ decades of combined experience in providing training, consulting, and computational tools to industrial experimenters. Supplying readers with the statistical means to analyze how numerous variables interact, it is ideal for those seeking breakthroughs in product quality and process efficiency via systematic experimentation.

    Following in the footsteps of its bestselling predecessors, this edition incorporates a lively approach to learning the fundamentals of the design of experiments (DOE). It lightens up the inherently dry complexities with interesting sidebars and amusing anecdotes.

    The book explains simple methods for collecting and displaying data and presents comparative experiments for testing hypotheses. Discussing how to block the sources of variation from your analysis, it looks at two-level factorial designs and covers analysis of variance. It also details a four-step planning process for designing and executing experiments that takes statistical power into consideration.

    This edition includes a major revision of the software that accompanies the book (via download) and sets the stage for introducing experiment designs where the randomization of one or more hard-to-change factors can be restricted. Along these lines, it includes a new chapter on split plots and adds coverage of a number of recent developments in the design and analysis of experiments.

    Readers have access to case studies, problems, practice experiments, a glossary of terms, and a glossary of statistical symbols, as well as a series of dynamic online lectures that cover the first several chapters of the book.

    Basic Statistics for DOE
    The "X" Factors
    Does Normal Distribution Ring Your Bell?
    Descriptive Statistics: Mean and Lean
    Confidence Intervals Help You Manage Expectations
    Graphical Tests Provide Quick Check for Normality
    Practice Problems

    Simple Comparative Experiments
    The F-Test Simplified
    A Dicey Situation: Making Sure They Are Fair
    Catching Cheaters with a Simple Comparative Experiment
    Blocking Out Known Sources of Variation
    Practice Problems

    Two-Level Factorial Design
    Two-Level Factorial Design: As Simple as Making Microwave Popcorn
    How to Plot and Interpret Interactions
    Protect Yourself with Analysis of Variance (ANOVA)
    Modeling Your Responses with Predictive Equations
    Diagnosing Residuals to Validate Statistical Assumptions
    Practice Problems
    Appendix: How to Make a More Useful Pareto Chart

    Dealing with Nonnormality via Response Transformations
    Skating on Thin Ice
    Log Transformation Saves the Data
    Choosing the Right Transformation
    Practice Problem

    Fractional Factorials
    Example of Fractional Factorial at Its Finest
    Potential Confusion Caused by Aliasing in Lower Resolution Factorials
    Plackett–Burman Designs
    Irregular Fractions Provide a Clearer View
    Practice Problem

    Getting the Most from Minimal-Run Designs
    Minimal-Resolution Design: The Dancing Raisin Experiment
    Complete Foldover of Resolution III Design
    Single-Factor Foldover
    Choose a High-Resolution Design to Reduce Aliasing Problems
    Practice Problems
    Appendix: Minimum-Run Designs for Screening

    General Multilevel Categoric Factorials
    Putting a Spring in Your Step: A General Factorial Design on Spring Toys
    How to Analyze Unreplicated General Factorials
    Practice Problems
    Appendix: Half-Normal Plot for General Factorial Designs
    Response Surface Methods for Optimization
    Center Points Detect Curvature in Confetti
    Augmenting to a Central Composite Design (CCD)
    Finding Your Sweet Spot for Multiple Responses

    Mixture Design
    Two-Component Mixture Design: Good as Gold
    Three-Component Design: Teeny Beany Experiment

    Back to the Basics: The Keys to Good DOE
    A Four-Step Process for Designing a Good Experiment
    A Case Study Showing Application of the Four-Step Design Process
    Appendix: Details on Power
         Managing Expectations for What the Experiment Might Reveal
         Increase the Range of Your Factors
         Decrease the Noise (σ) in Your System
         Accept Greater Risk of Type I Error (α)
         Select a Better and/or Bigger Design

    Split-Plot Designs to Accommodate Hard-to-Change Factors
    How Split Plots Naturally Emerged for Agricultural Field Tests
    Applying a Split Plot to Save Time Making Paper Helicopters
    Trade-Off of Power for Convenience When Restricting Randomization
    One More Split Plot Example: A Heavy-Duty Industrial One

    Practice Experiments
    Practice Experiment #1: Breaking Paper Clips
    Practice Experiment #2: Hand–Eye Coordination
    Other Fun Ideas for Practice Experiments
         Ball in Funnel
         Flight of the Balsa Buzzard
         Paper Airplanes
         Impact Craters

    Appendix 1
    Two-Tailed t-Table
    F-Table for 10%
    F-Table for 5%
    F-Table for 1%
    F-Table for 0.1%

    Appendix 2
    Four-Factor Screening and Characterization Designs
         Screening Main Effects in 8 Runs
         Screening Design Layout
         Alias Structure
         Characterizing Interactions with 12 Runs
         Characterization Design Layout
         Alias Structure for Factorial Two-Factor Interaction Model
         Alias Structure for Factorial Main Effect Model
    Five-Factor Screening and Characterization Designs
         Screening Main Effects in 12 Runs
         Screening Design Layout
         Alias Structure
         Characterizing Interactions with 16 Runs
         Design Layout
         Alias Structure for Factorial Two-Factor Interaction (2FI) Model
    Six-Factor Screening and Characterization Designs
         Screening Main Effects in 14 Runs
         Screening Design Layout
         Alias Structure
         Characterizing Interactions with 22 Runs
         Design Layout
         Alias Structure for Factorial Two-Factor Interaction (2FI) Model
    Seven-Factor Screening and Characterization Designs
         Screening Design Layout
         Alias Structure
         Characterizing Interactions with 30 Runs
         Design Layout
         Alias Structure for Factorial Two-Factor Interaction (2FI) Model

    Statistical Symbols

    Recommended Readings
    Case Study Articles



    Mark J. Anderson, PE, CQE, MBA, is a principal and general manager of Stat-Ease, Inc. in Minneapolis, Minnesota. A chemical engineer by profession, he also has a diverse array of experience in process development (earning a patent), quality assurance, marketing, purchasing, and general management. Prior to joining Stat-Ease, he spearheaded an award-winning quality improvement program (which generated millions of dollars in profit for an international manufacturer) and served as general manager for a medical device manufacturer. His other achievements include an extensive portfolio of published articles on design of experiments. Anderson co-authored (with Whitcomb) RSM Simplified: Optimizing Processes Using Response Surface Methods for Design of Experiments (Productivity Press, 2004).

    Patrick J. Whitcomb, PE, MS, is the founding principal and president of Stat-Ease, Inc. Before starting his own business, he worked as a chemical engineer, quality assurance manager, and plant manager. Whitcomb developed Design-Ease® software, an easy-to-use program for design of two-level and general factorial experiments, and Design-Expert® software, an advanced user’s program for response surface, mixture, and combined designs. He has provided consulting and training on the application of design of experiments (DOE) and other statistical methods for decades. In 2013, the Minnesota Federation of Engineering, Science, and Technology Societies (MFESTS) awarded Whitcomb the Charles W. Britzius Distinguished Engineer Award for his lifetime achievements.