DOE Simplified: Practical Tools for Effective Experimentation, Second Edition

Offering a planned approach for determining cause and effect, this work provides researchers with the statistical means to analyze how numerous variables interact. With this second edition of DOE Simplified, Mark Anderson and Patrick Whitcomb retain their lively approach to the learning of fundamentals. They lighten up the inherently dry complexities with interesting sidebars and amusing anecdotes. In this edition, they add new and updated information, including a four-step planning process for designing and executing experiments with a consideration of statistical power. Accompanying this book is a 180-day trial for the new version of Stat-Ease's software: Design-Ease®,v. 7. This educational program assists with mathematic computations, so as to allow one to focus on analysis of the data.

Table of Contents:
Flowchart Guide to DOE Simplified Chapter 1: 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 Chapter 2: Simple Comparative Experiments F-Test Simplified A Dicey Situation - Making Sure They're Fair Catching Cheaters with a Simple Comparative Experiment Blocking Out Known Sources of Variation Practice Problems Chapter 3: 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 Chapter 4: Dealing with Non-Normality via Response Transformations Skating on Thin Ice Log Transformation Saves the Data Choosing the Right Transformation Practice Problem Chapter 5: 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 Chapter 6: 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 Chapter 7: General Factorial Designs 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 Chapter 8: 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 Chapter 9: Mixture Design Two-Component Mixture Design: Good as Gold Three-Component Design: Teeny Beany Experiment Chapter 10: 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 Chapter 11: Practice Experiments Practice Experiment #1: Breaking Paper Clips Practice Experiment #2: Hand-eye Coordination Other Fun Ideas for Practice Experiments Appendices Glossary Glossary of Statistical Symbols Glossary of Terms