DOE Simplified Practical Tools for Effective Experimentation

ISBN-10: 1563273446

ISBN-13: 9781563273445

Edition: 2nd 2007 (Revised)

Authors: Mark J. Anderson, Patrick J. Whitcomb

List price: $62.95
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Design of Experiments (DOE) provides a statistical means for analyzing how numerous variables interact. The tool is a planned approach for determining cause and effect relationships.
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Book details

List price: $62.95
Edition: 2nd
Copyright year: 2007
Publisher: Productivity Press
Publication date: 7/30/2007
Binding: Paperback
Pages: 256
Size: 7.25" wide x 9.25" long x 0.75" tall
Weight: 0.946
Language: English

What's New in This Edition
Flowchart Guide to DOE Simplified
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're 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
How to Make a More Useful Pareto Chart
Dealing with Non-Normality 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
Minimum-Run Designs for Screening
General Factorial Designs
Putting a Spring in Your Step-A General Factorial Design on Spring Toys
How to Analyze Unreplicated General Factorials
Practice Problems
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
Details on Power
Practice Experiments
Breaking Paper Clips
Hand-Eye Coordination
Other Fun Ideas for Practice Experiments
Two-tailed t-Table
F-Table for 10%
F-Table for 5%
F-Table for 1%
F-Table for 0.1%
Four-Factor Screening Design
Five-Factor Screening Design
Six-Factor Screening Design
Seven-Factor Screening Design
Glossary of Statistical Symbols
Glossary of Terms
Recommended Readings
About the Software
About the Authors
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