Overview

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.

Full Product Details

Author:   Mark J. Anderson ,  Patrick J. Whitcomb
Publisher:   Taylor & Francis Ebooks
Imprint:   Productivity Press
Edition:   3rd Revised edition
ISBN:  

9781498730907

ISBN 10:   1498730906
Pages:   268
Publication Date:   21 May 2015
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Electronic book text
Publisher's Status:   Active
Availability:   Available To Order  
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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 (sigma) in Your System Accept Greater Risk of Type I Error (alpha) 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

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Author Information

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(R) software, an easy-to-use program for design of two-level and general factorial experiments, and Design-Expert(R) 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.

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