3rd Edition
DOE Simplified Practical Tools for Effective Experimentation, Third Edition
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
Glossary
Statistical Symbols
Terms
Recommended Readings
Textbooks
Case Study Articles
Index
Biography
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.
