DOE simplified : practical tools for effective experimentation
著者
書誌事項
DOE simplified : practical tools for effective experimentation
Productivity Press, c2007
2nd ed
- : pbk
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注記
Includes bibliographical references (p. 223-224) and index
HTTP:URL=http://www.loc.gov/catdir/toc/ecip0719/2007022642.html Information=Table of contents only
内容説明・目次
内容説明
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 (R),v. 7. This educational program assists with mathematic computations, so as to allow one to focus on analysis of the data.
目次
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
「Nielsen BookData」 より
