Planning and Executing Credible Experiments. Robert J. Moffat
Original Data, Summary, and R References Homework
16 8 Exploring Statistical Design of Experiments 8.1 Always Seeking Wiser Strategies 8.2 Evolving from Novice Experiment Design 8.3 Two‐Level and Three‐Level Factorial Experiment Plans 8.4 A Three‐Level, Three‐Factor Design 8.5 The Plackett–Burman 12‐Run Screening Design 8.6 Details About Analysis of Statistically Designed Experiments 8.7 Retrospect of Statistical Design Examples 8.8 Philosophy of Statistical Design 8.9 Statistical Design for Conditions That Challenge Factorial Designs 8.10 A Highly Recommended Tool for Statistical Design of Experiments 8.11 More Tools for Statistical Design of Experiments 8.12 Conclusion Further Reading Homework
17 9 Selecting the Data Points 9.1 The Three Categories of Data 9.2 Populating the Operating Volume 9.3 Example from Velocimetry 9.4 Organize the Data 9.5 Strategies to Select Next Data Points 9.6 Demonstrate Gosset for Selecting Data 9.7 Use Gosset to Analyze Results 9.8 Other Options and Features of Gosset 9.9 Using Gosset to Find Local Extrema in a Function of Several Variables 9.10 Summary Further Reading Homework
18 10 Analyzing Measurement Uncertainty 10.1 Clarifying Uncertainty Analysis 10.2 Definitions 10.3 The Sources and Types of Errors 10.4 The Basic Mathematics 10.5 Automating the Uncertainty Analysis 10.6 Single‐Sample Uncertainty Analysis References Further Reading Homework
19 11 Using Uncertainty Analysis in Planning and Execution 11.1 Using Uncertainty Analysis in Planning 11.2 Perform Uncertainty Analysis on the DREs 11.3 Using Uncertainty Analysis in Selecting Instruments 11.4 Using Uncertainty Analysis in Debugging an Experiment 11.5 Reporting the Uncertainties in an Experiment 11.6 Multiple‐Sample Uncertainty Analysis 11.7 Coordinate with Uncertainty Analysis Standards 11.8 Describing the Overall Uncertainty in a Single Measurement 11.9 Additional Statistical Tools and Elements References Homework
20 12 Debugging an Experiment, Shakedown, and Validation 12.1 Introduction 12.2 Classes of Error 12.3 Using Time‐Series Analysis in Debugging 12.4 Examples 12.5 Process Unsteadiness 12.6 The Effect of Time‐Constant Mismatching 12.7 Using Uncertainty Analysis in Debugging an Experiment 12.8 Debugging the Experiment via the Data Interpretation Program 12.9 Situational Uncertainty
21 13 Trimming Uncertainty 13.1 Focusing on the Goal 13.2 A Mlotivating Question for Industrial Production 13.3 Plackett–Burman 12‐Run Results and Motivating Question #3 13.4 PB 12‐Run Results and Motivating Question #1 13.5 Uncertainty Analysis of Dual Predictive Model and Motivating Question #2 13.6 The PB 12‐Run Results and Individual Machine Models 13.7 Final Answers to All Motivating Questions for the PB Example Experiment 13.8 Conclusions
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