DOE Simplified: Practical Tools for Effective Experimentation, Third Edition, 3rd Edition (Paperback) book cover

DOE Simplified

Practical Tools for Effective Experimentation, Third Edition, 3rd Edition

By Mark J. Anderson, Patrick J. Whitcomb

Productivity Press

268 pages | 85 B/W Illus.

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Description

Offering a planned approach for determining cause and effect, DOE Simplified: Practical Tools for Effective Experimentation, Third Editionintegrates 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

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

About the Authors

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.

Subject Categories

BISAC Subject Codes/Headings:
BUS053000
BUSINESS & ECONOMICS / Quality Control
TEC020000
TECHNOLOGY & ENGINEERING / Manufacturing