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| Preface | |
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| What's New in This Edition | |
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| Introduction | |
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| Flowchart Guide to DOE Simplified | |
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| Basic Statistics for DOE | |
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| The "X" Factors | |
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| Does Normal Distribution Ring Your Bell? | |
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| Descriptive Statistics-Mean and Lean | |
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| Confidence Intervals Help You Manage Expectations | |
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| Graphical Tests Provide Quick Check for Normality | |
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| Practice Problems | |
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| Simple Comparative Experiments | |
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| The F-Test Simplified | |
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| A Dicey Situation-Making Sure They're Fair | |
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| Catching Cheaters with a Simple Comparative Experiment | |
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| Blocking Out Known Sources of Variation | |
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| Practice Problems | |
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| Two-Level Factorial Design | |
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| Two-Level Factorial Design-As Simple as Making Microwave Popcorn | |
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| How to Plot and Interpret Interactions | |
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| Protect Yourself with Analysis of Variance (ANOVA) | |
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| Modeling Your Responses with Predictive Equations | |
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| Diagnosing Residuals to Validate Statistical Assumptions | |
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| Practice Problems | |
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| How to Make a More Useful Pareto Chart | |
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| Dealing with Non-Normality via Response Transformations | |
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| Skating on Thin Ice | |
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| Log Transformation Saves the Data | |
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| Choosing the Right Transformation | |
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| Practice Problem | |
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| Fractional Factorials | |
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| Example of Fractional Factorial at Its Finest | |
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| Potential Confusion Caused by Aliasing in Lower Resolution Factorials | |
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| Plackett-Burman Designs | |
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| Irregular Fractions Provide a Clearer View | |
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| Practice Problem | |
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| Getting the Most from Minimal-Run Designs | |
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| Minimal-Resolution Design: The Dancing-Raisin Experiment | |
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| Complete Foldover of Resolution III Design | |
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| Single-Factor Foldover | |
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| Choose a High-Resolution Design to Reduce Aliasing Problems | |
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| Practice Problems | |
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| Minimum-Run Designs for Screening | |
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| General Factorial Designs | |
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| Putting a Spring in Your Step-A General Factorial Design on Spring Toys | |
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| How to Analyze Unreplicated General Factorials | |
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| Practice Problems | |
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| Half-Normal Plot for General Factorial Designs | |
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| Response Surface Methods for Optimization | |
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| Center Points Detect Curvature in Confetti | |
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| Augmenting to a Central Composite Design (CCD) | |
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| Finding Your Sweet Spot for Multiple Responses | |
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| Mixture Design | |
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| Two-Component Mixture Design: Good as Gold | |
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| Three-Component Design: Teeny Beany Experiment | |
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| Back to the Basics-The Keys to Good DOE | |
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| A Four-Step Process for Designing a Good Experiment | |
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| A Case Study Showing Application of the Four-Step Design Process | |
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| Details on Power | |
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| Practice Experiments | |
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| Breaking Paper Clips | |
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| Hand-Eye Coordination | |
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| Other Fun Ideas for Practice Experiments | |
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| Appendices | |
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| Two-tailed t-Table | |
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| F-Table for 10% | |
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| F-Table for 5% | |
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| F-Table for 1% | |
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| F-Table for 0.1% | |
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| Four-Factor Screening Design | |
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| Five-Factor Screening Design | |
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| Six-Factor Screening Design | |
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| Seven-Factor Screening Design | |
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| Glossary | |
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| Glossary of Statistical Symbols | |
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| Glossary of Terms | |
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| Recommended Readings | |
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| Index | |
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| About the Software | |
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| About the Authors | |