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DOE Simplified, 3rd Edition

Book Description

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.

Table of Contents

  1. Preface
    1. What’s New in This Edition
  2. Introduction
  3. Chapter 1 - Basic Statistics for DOE
    1. The “X” Factors
    2. Does Normal Distribution Ring Your Bell?
    3. Descriptive Statistics: Mean and Lean
    4. Confidence Intervals Help You Manage Expectations
    5. Graphical Tests Provide Quick Check for Normality
    6. Practice Problems
      1. Problem 1.1
      2. Problem 1.2
  4. Chapter 2 - Simple Comparative Experiments
    1. The F-Test Simplified
    2. A Dicey Situation: Making Sure They Are Fair
    3. Catching Cheaters with a Simple Comparative Experiment
    4. Blocking Out Known Sources of Variation
    5. Practice Problems
      1. Problem 2.1
      2. Problem 2.2
      3. Problem 2.3
      4. Problem 2.4
  5. Chapter 3 - Two-Level Factorial Design
    1. Two-Level Factorial Design: As Simple as Making Microwave Popcorn
    2. How to Plot and Interpret Interactions
    3. Protect Yourself with Analysis of Variance (ANOVA)
    4. Modeling Your Responses with Predictive Equations
    5. Diagnosing Residuals to Validate Statistical Assumptions
    6. Practice Problems
      1. Problem 3.1
      2. Problem 3.2
    7. Appendix: How to Make a More Useful Pareto Chart
  6. Chapter 4 - Dealing with Nonnormality via Response Transformations
    1. Skating on Thin Ice
    2. Log Transformation Saves the Data
    3. Choosing the Right Transformation
    4. Practice Problem
      1. Problem 4.1
  7. Chapter 5 - Fractional Factorials
    1. Example of Fractional Factorial at Its Finest
    2. Potential Confusion Caused by Aliasing in Lower Resolution Factorials
    3. Plackett–Burman Designs
    4. Irregular Fractions Provide a Clearer View
    5. Practice Problem
      1. Problem 5.1
  8. Chapter 6 - Getting the Most from Minimal-Run Designs
    1. Minimal-Resolution Design: The Dancing Raisin Experiment
    2. Complete Foldover of Resolution III Design
    3. Single-Factor Foldover
    4. Choose a High-Resolution Design to Reduce Aliasing Problems
    5. Practice Problems
      1. Problem 6.1
      2. Problem 6.2
    6. Appendix: Minimum-Run Designs for Screening
  9. Chapter 7 - General Multilevel Categoric Factorials
    1. Putting a Spring in Your Step: A General Factorial Design on Spring Toys
    2. How to Analyze Unreplicated General Factorials
    3. Practice Problems
      1. Problem 7.1
      2. Problem 7.2
    4. Appendix: Half-Normal Plot for General Factorial Designs
  10. Chapter 8 - Response Surface Methods for Optimization
    1. Center Points Detect Curvature in Confetti
    2. Augmenting to a Central Composite Design (CCD)
    3. Finding Your Sweet Spot for Multiple Responses
  11. Chapter 9 - Mixture Design
    1. Two-Component Mixture Design: Good as Gold
    2. Three-Component Design: Teeny Beany Experiment
  12. Chapter 10 - Back to the Basics: The Keys to Good DOE
    1. A Four-Step Process for Designing a Good Experiment
    2. A Case Study Showing Application of the Four-Step Design Process
    3. Appendix: Details on Power
      1. Managing Expectations for What the Experiment Might Reveal
      2. Increase the Range of Your Factors
      3. Decrease the Noise (σ) in Your System
      4. Accept Greater Risk of Type I Error (α)
      5. Select a Better and/or Bigger Design
  13. Chapter 11 - Split-Plot Designs to Accommodate Hard-to-Change Factors
    1. How Split Plots Naturally Emerged for Agricultural Field Tests
    2. Applying a Split Plot to Save Time Making Paper Helicopters
    3. Trade-Off of Power for Convenience When Restricting Randomization
    4. One More Split Plot Example: A Heavy-Duty Industrial One
  14. Chapter 12 - Practice Experiments
    1. Practice Experiment #1: Breaking Paper Clips
    2. Practice Experiment #2: Hand–Eye Coordination
    3. Other Fun Ideas for Practice Experiments
      1. Ball in Funnel
      2. Flight of the Balsa Buzzard
      3. Paper Airplanes
      4. Impact Craters
  15. Appendix 1
    1. A1.1 Two-Tailed t-Table
    2. A1.2 F-Table for 10%
    3. A1.3 F-Table for 5%
    4. A1.4 F-Table for 1%
    5. A1.5 F-Table for 0.1%
  16. Appendix 2
    1. A2.1 Four-Factor Screening and Characterization Designs
      1. Screening Main Effects in 8 Runs
      2. Screening Design Layout
      3. Alias Structure
      4. Characterizing Interactions with 12 Runs
      5. Characterization Design Layout
      6. Alias Structure for Factorial Two-Factor Interaction Model
      7. Alias Structure for Factorial Main Effect Model
    2. A2.2 Five-Factor Screening and Characterization Designs
      1. Screening Main Effects in 12 Runs
      2. Screening Design Layout
      3. Alias Structure
      4. Characterizing Interactions with 16 Runs
      5. Design Layout
      6. Alias Structure for Factorial Two-Factor Interaction (2FI) Model
    3. A2.3 Six-Factor Screening and Characterization Designs
      1. Screening Main Effects in 14 Runs
      2. Screening Design Layout
      3. Alias Structure
      4. Characterizing Interactions with 22 Runs
      5. Design Layout
      6. Alias Structure for Factorial Two-Factor Interaction (2FI) Model
    4. A2.4 Seven-Factor Screening and Characterization Designs
      1. Screening Main Effects in 16 Runs
      2. Screening Design Layout
      3. Alias Structure
      4. Characterizing Interactions with 30 Runs
      5. Design Layout
      6. Alias Structure for Factorial Two-Factor Interaction (2FI) Model
  17. Glossary
    1. Statistical Symbols
    2. Terms
  18. Recommended Readings
    1. Textbooks
    2. Case Study Articles
  19. About the Authors
  20. About the Software