{"product_id":"planning-and-executing-credible-experiments-9781119532873","title":"Planning and Executing Credible Experiments","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eCovers experiment planning, execution, analysis, and reporting\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThis single-source resource guides readers in planning and conducting credible experiments for engineering, science, industrial processes, agriculture, and business. The text takes experimenters all the way through conducting a high-impact experiment, from initial conception, through execution of the experiment, to a defensible final report. It prepares the reader to anticipate the choices faced during each stage.\u003c\/p\u003e \u003cp\u003eFilled with real-world examples from engineering science and industry, \u003ci\u003ePlanning and Executing Credible Experiments: A Guidebook for Engineering, Science, Industrial Processes, Agriculture, and Business\u003c\/i\u003e offers chapters that challenge experimenters at each stage of planning and execution and emphasizes uncertainty analysis as a design tool in addition to its role for reporting results. Tested over decades at Stanford University and internationally, the text employs two powerful, free, \u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eAbout the Authors xxi\u003c\/p\u003e \u003cp\u003ePreface xxiii\u003c\/p\u003e \u003cp\u003eAcknowledgments xxvii\u003c\/p\u003e \u003cp\u003eAbout the Companion Website xxix\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Choosing Credibility 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 The Responsibility of an Experimentalist 2\u003c\/p\u003e \u003cp\u003e1.2 Losses of Credibility 2\u003c\/p\u003e \u003cp\u003e1.3 Recovering Credibility 3\u003c\/p\u003e \u003cp\u003e1.4 Starting with a Sharp Axe 3\u003c\/p\u003e \u003cp\u003e1.5 A Systems View of Experimental Work 4\u003c\/p\u003e \u003cp\u003e1.6 In Defense of Being a Generalist 5\u003c\/p\u003e \u003cp\u003ePanel 1.1 The Bundt Cake Story 6\u003c\/p\u003e \u003cp\u003eReferences 6\u003c\/p\u003e \u003cp\u003eHomework 6\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 The Nature of Experimental Work 7\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Tested Guide of Strategy and Tactics 7\u003c\/p\u003e \u003cp\u003e2.2 What Can Be Measured and What Cannot? 8\u003c\/p\u003e \u003cp\u003e2.2.1 Examples Not Measurable 8\u003c\/p\u003e \u003cp\u003e2.2.2 Shapes 9\u003c\/p\u003e \u003cp\u003e2.2.3 Measurable by the Human Sensory System 10\u003c\/p\u003e \u003cp\u003e2.2.4 Identifying and Selecting Measurable Factors 11\u003c\/p\u003e \u003cp\u003e2.2.5 Intrusive Measurements 11\u003c\/p\u003e \u003cp\u003e2.3 Beware Measuring Without Understanding: Warnings from History 12\u003c\/p\u003e \u003cp\u003e2.4 How Does Experimental Work Differ from Theory and Analysis? 13\u003c\/p\u003e \u003cp\u003e2.4.1 Logical Mode 13\u003c\/p\u003e \u003cp\u003e2.4.2 Persistence 13\u003c\/p\u003e \u003cp\u003e2.4.3 Resolution 13\u003c\/p\u003e \u003cp\u003e2.4.4 Dimensionality 15\u003c\/p\u003e \u003cp\u003e2.4.5 Similarity and Dimensional Analysis 15\u003c\/p\u003e \u003cp\u003e2.4.6 Listening to Our Theoretician Compatriots 16\u003c\/p\u003e \u003cp\u003ePanel 2.1 Positive Consequences of the Reproducibility Crisis 17\u003c\/p\u003e \u003cp\u003ePanel 2.2 Selected Invitations to Experimental Research, Insights from Theoreticians 18\u003c\/p\u003e \u003cp\u003ePanel 2.3 Prepublishing Your Experiment Plan 21\u003c\/p\u003e \u003cp\u003e2.4.7 Surveys and Polls 22\u003c\/p\u003e \u003cp\u003e2.5 Uncertainty 23\u003c\/p\u003e \u003cp\u003e2.6 Uncertainty Analysis 23\u003c\/p\u003e \u003cp\u003eReferences 24\u003c\/p\u003e \u003cp\u003eHomework 25\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 An Overview of Experiment Planning 27\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Steps in an Experimental Plan 27\u003c\/p\u003e \u003cp\u003e3.2 Iteration and Refinement 28\u003c\/p\u003e \u003cp\u003e3.3 Risk Assessment\/Risk Abatement 28\u003c\/p\u003e \u003cp\u003e3.4 Questions to Guide Planning of an Experiment 29\u003c\/p\u003e \u003cp\u003eHomework 30\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Identifying the Motivating Question 31\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 The Prime Need 31\u003c\/p\u003e \u003cp\u003ePanel 4.1 There’s a Hole in My Bucket 32\u003c\/p\u003e \u003cp\u003e4.2 An Anchor and a Sieve 33\u003c\/p\u003e \u003cp\u003e4.3 Identifying the Motivating Question Clarifies Thinking 33\u003c\/p\u003e \u003cp\u003e4.3.1 Getting Started 33\u003c\/p\u003e \u003cp\u003e4.3.2 Probe and Focus 34\u003c\/p\u003e \u003cp\u003e4.4 Three Levels of Questions 35\u003c\/p\u003e \u003cp\u003e4.5 Strong Inference 36\u003c\/p\u003e \u003cp\u003e4.6 Agree on the Form of an Acceptable Answer 36\u003c\/p\u003e \u003cp\u003e4.7 Specify the Allowable Uncertainty 37\u003c\/p\u003e \u003cp\u003e4.8 Final Closure 37\u003c\/p\u003e \u003cp\u003eReference 38\u003c\/p\u003e \u003cp\u003eHomework 38\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Choosing the Approach 39\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Laying Groundwork 39\u003c\/p\u003e \u003cp\u003e5.2 Experiment Classifications 40\u003c\/p\u003e \u003cp\u003e5.2.1 Exploratory 40\u003c\/p\u003e \u003cp\u003e5.2.2 Identifying the Important Variables 40\u003c\/p\u003e \u003cp\u003e5.2.3 Demonstration of System Performance 41\u003c\/p\u003e \u003cp\u003e5.2.4 Testing a Hypothesis 41\u003c\/p\u003e \u003cp\u003e5.2.5 Developing Constants for Predetermined Models 41\u003c\/p\u003e \u003cp\u003e5.2.6 Custody Transfer and System Performance Certification Tests 42\u003c\/p\u003e \u003cp\u003e5.2.7 Quality-Assurance Tests 42\u003c\/p\u003e \u003cp\u003e5.2.8 Summary 43\u003c\/p\u003e \u003cp\u003e5.3 Real or Simplified Conditions? 43\u003c\/p\u003e \u003cp\u003e5.4 Single-Sample or Multiple-Sample? 43\u003c\/p\u003e \u003cp\u003ePanel 5.1 A Brief Summary of “Dissertation upon Roast Pig” 44\u003c\/p\u003e \u003cp\u003ePanel 5.2 Consider a Spherical Cow 44\u003c\/p\u003e \u003cp\u003e5.5 Statistical or Parametric Experiment Design? 45\u003c\/p\u003e \u003cp\u003e5.6 Supportive or Refutative? 47\u003c\/p\u003e \u003cp\u003e5.7 The Bottom Line 47\u003c\/p\u003e \u003cp\u003eReferences 48\u003c\/p\u003e \u003cp\u003eHomework 48\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Mapping for Safety, Operation, and Results 51\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Construct Multiple Maps to Illustrate and Guide Experiment Plan 51\u003c\/p\u003e \u003cp\u003e6.2 Mapping Prior Work and Proposed Work 51\u003c\/p\u003e \u003cp\u003e6.3 Mapping the Operable Domain of an Apparatus 53\u003c\/p\u003e \u003cp\u003e6.4 Mapping in Operator’s Coordinates 57\u003c\/p\u003e \u003cp\u003e6.5 Mapping the Response Surface 59\u003c\/p\u003e \u003cp\u003e6.5.1 Options for Organizing a Table 59\u003c\/p\u003e \u003cp\u003e6.5.2 Options for Presenting the Response on a Scatter-Plot-Type Graph 61\u003c\/p\u003e \u003cp\u003eHomework 64\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Refreshing Statistics 65\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Reviving Key Terms to Quantify Uncertainty 65\u003c\/p\u003e \u003cp\u003e7.1.1 Population 65\u003c\/p\u003e \u003cp\u003e7.1.2 Sample 66\u003c\/p\u003e \u003cp\u003e7.1.3 Central Value 67\u003c\/p\u003e \u003cp\u003e7.1.4 Mean, μ or \u003ci\u003eȲ \u003c\/i\u003e67\u003c\/p\u003e \u003cp\u003e7.1.5 Residual 67\u003c\/p\u003e \u003cp\u003e7.1.6 Variance, σ\u003csup\u003e2\u003c\/sup\u003e or \u003ci\u003eS\u003c\/i\u003e\u003csup\u003e2\u003c\/sup\u003e 68\u003c\/p\u003e \u003cp\u003e7.1.7 Degrees of Freedom, \u003ci\u003eDf \u003c\/i\u003e68\u003c\/p\u003e \u003cp\u003e7.1.8 Standard Deviation, σ\u003ci\u003e\u003csub\u003eY\u003c\/sub\u003e \u003c\/i\u003eor \u003ci\u003eS\u003csub\u003eY\u003c\/sub\u003e \u003c\/i\u003e68\u003c\/p\u003e \u003cp\u003e7.1.9 Uncertainty of the Mean, δμ 69\u003c\/p\u003e \u003cp\u003e7.1.10 Chi‐Squared, \u003ci\u003eχ\u003c\/i\u003e\u003csup\u003e2\u003c\/sup\u003e 69\u003c\/p\u003e \u003cp\u003e7.1.11 p‐Value 70\u003c\/p\u003e \u003cp\u003e7.1.12 Null Hypothesis 70\u003c\/p\u003e \u003cp\u003e7.1.13 F‐value of Fisher Statistic 71\u003c\/p\u003e \u003cp\u003e7.2 The Data Distribution Most Commonly Encountered The Normal Distribution for Samples of Infinite Size 71\u003c\/p\u003e \u003cp\u003e7.3 Account for Small Samples: The t‐Distribution 72\u003c\/p\u003e \u003cp\u003e7.4 Construct Simple Models by Computer to Explain the Data 73\u003c\/p\u003e \u003cp\u003e7.4.1 Basic Statistical Analysis of Quantitative Data 73\u003c\/p\u003e \u003cp\u003e7.4.2 Model Data Containing Categorical and Quantitative Factors 75\u003c\/p\u003e \u003cp\u003e7.4.3 Display Data Fit to One Categorical Factor and One Quantitative Factor 76\u003c\/p\u003e \u003cp\u003e7.4.4 Quantify How Each Factor Accounts for Variation in the Data 76\u003c\/p\u003e \u003cp\u003e7.5 Gain Confidence and Skill at Statistical Modeling Via the R Language 77\u003c\/p\u003e \u003cp\u003e7.5.1 Model and Plot Results of a Single Variable Using the Example Data diceshoe.csv 77\u003c\/p\u003e \u003cp\u003e7.5.2 Evaluate Alternative Models of the Example Data hiloy.csv 78\u003c\/p\u003e \u003cp\u003e7.5.2.1 Inspect the Data 78\u003c\/p\u003e \u003cp\u003e7.5.3 Grand Mean 78\u003c\/p\u003e \u003cp\u003e7.5.4 Model by Groups: Group‐Wise Mean 78\u003c\/p\u003e \u003cp\u003e7.5.5 Model by a Quantitative Factor 78\u003c\/p\u003e \u003cp\u003e7.5.6 Model by Multiple Quantitative Factors 78\u003c\/p\u003e \u003cp\u003e7.5.7 Allow Factors to Interact (So Each Group Gets Its Own Slope) 79\u003c\/p\u003e \u003cp\u003e7.5.8 Include Polynomial Factors (a Statistical Linear Model Can Be Curved) 80\u003c\/p\u003e \u003cp\u003e7.6 Report Uncertainty 80\u003c\/p\u003e \u003cp\u003e7.7 Decrease Uncertainty (Improve Credibility) by Isolating Distinct Groups 81\u003c\/p\u003e \u003cp\u003e7.8 Original Data, Summary, and R 82\u003c\/p\u003e \u003cp\u003eReferences 83\u003c\/p\u003e \u003cp\u003eHomework 83\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Exploring Statistical Design of Experiments 87\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Always Seeking Wiser Strategies 87\u003c\/p\u003e \u003cp\u003e8.2 Evolving from Novice Experiment Design 87\u003c\/p\u003e \u003cp\u003e8.3 Two‐Level and Three‐Level Factorial Experiment Plans 88\u003c\/p\u003e \u003cp\u003e8.4 A Three‐Level, Three‐Factor Design 89\u003c\/p\u003e \u003cp\u003e8.5 The Plackett–Burman 12‐Run Screening Design 93\u003c\/p\u003e \u003cp\u003e8.6 Details About Analysis of Statistically Designed Experiments 95\u003c\/p\u003e \u003cp\u003e8.6.1 Model Main Factors to Original Raw Data 95\u003c\/p\u003e \u003cp\u003e8.6.2 Model Main Factors to Original Data Around Center of Each Factor 96\u003c\/p\u003e \u003cp\u003e8.6.3 Model Including All Interaction Terms 97\u003c\/p\u003e \u003cp\u003e8.6.4 Model Including Only Dominant Interaction Terms 97\u003c\/p\u003e \u003cp\u003e8.6.5 Model Including Dominant Interaction Term Plus Quadratic Term 98\u003c\/p\u003e \u003cp\u003e8.6.6 Model All Factors of Example 2, Centering Each Quantitative Factor 99\u003c\/p\u003e \u003cp\u003e8.6.7 Refine Model of Example 2 Including Only Dominant Terms 100\u003c\/p\u003e \u003cp\u003e8.7 Retrospect of Statistical Design Examples 101\u003c\/p\u003e \u003cp\u003e8.8 Philosophy of Statistical Design 101\u003c\/p\u003e \u003cp\u003e8.9 Statistical Design for Conditions That Challenge Factorial Designs 102\u003c\/p\u003e \u003cp\u003e8.10 A Highly Recommended Tool for Statistical Design of Experiments 103\u003c\/p\u003e \u003cp\u003e8.11 More Tools for Statistical Design of Experiments 103\u003c\/p\u003e \u003cp\u003e8.12 Conclusion 103\u003c\/p\u003e \u003cp\u003eFurther Reading 104\u003c\/p\u003e \u003cp\u003eHomework 104\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Selecting the Data Points 107\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 The Three Categories of Data 107\u003c\/p\u003e \u003cp\u003e9.1.1 The Output Data 107\u003c\/p\u003e \u003cp\u003e9.1.2 Peripheral Data 108\u003c\/p\u003e \u003cp\u003e9.1.3 Backup Data 108\u003c\/p\u003e \u003cp\u003e9.1.4 Other Data You May Wish to Acquire 108\u003c\/p\u003e \u003cp\u003e9.2 Populating the Operating Volume 109\u003c\/p\u003e \u003cp\u003e9.2.1 Locating the Data Points Within the Operating Volume 109\u003c\/p\u003e \u003cp\u003e9.2.2 Estimating the Topography of the Response Surface 109\u003c\/p\u003e \u003cp\u003e9.3 Example from Velocimetry 109\u003c\/p\u003e \u003cp\u003e9.3.1 Sharpen Our Approach 110\u003c\/p\u003e \u003cp\u003e9.3.2 Lessons Learned from Velocimetry Example 111\u003c\/p\u003e \u003cp\u003e9.4 Organize the Data 112\u003c\/p\u003e \u003cp\u003e9.4.1 Keep a Laboratory Notebook 112\u003c\/p\u003e \u003cp\u003e9.4.2 Plan for Data Security 112\u003c\/p\u003e \u003cp\u003e9.4.3 Decide Data Format 112\u003c\/p\u003e \u003cp\u003e9.4.4 Overview Data Guidelines 112\u003c\/p\u003e \u003cp\u003e9.4.5 Reasoning Through Data Guidelines 113\u003c\/p\u003e \u003cp\u003e9.5 Strategies to Select Next Data Points 114\u003c\/p\u003e \u003cp\u003e9.5.1 Overview of Option 1: Default Strategy with Intensive Experimenter Involvement 115\u003c\/p\u003e \u003cp\u003e9.5.1.1 Choosing the Data Trajectory 115\u003c\/p\u003e \u003cp\u003e9.5.1.2 The Default Strategy: Be Bold 115\u003c\/p\u003e \u003cp\u003e9.5.1.3 Anticipate, Check, Course Correct 116\u003c\/p\u003e \u003cp\u003e9.5.1.4 Other Aspects to Keep in Mind 116\u003c\/p\u003e \u003cp\u003e9.5.1.5 Endpoints 117\u003c\/p\u003e \u003cp\u003e9.5.2 Reintroducing Gosset 118\u003c\/p\u003e \u003cp\u003e9.5.3 Practice Gosset Examples (from Gosset User Manual) 119\u003c\/p\u003e \u003cp\u003e9.6 Demonstrate Gosset for Selecting Data 120\u003c\/p\u003e \u003cp\u003e9.6.1 Status Quo of Experiment Planning and Execution (Prior to Selecting More Samples) 120\u003c\/p\u003e \u003cp\u003e9.6.1.1 Specified Motivating Question 120\u003c\/p\u003e \u003cp\u003e9.6.1.2 Identified Pertinent Candidate Factors 121\u003c\/p\u003e \u003cp\u003e9.6.1.3 Selected Initial Sample Points Using Plackett–Burman 121\u003c\/p\u003e \u003cp\u003e9.6.1.4 Executed the First 12 Runs at the PB Sample Conditions 122\u003c\/p\u003e \u003cp\u003e9.6.1.5 Analyzed Results. Identified Dominant First-Order Factors. Estimated First-Order Uncertainties of Factors 123\u003c\/p\u003e \u003cp\u003e9.6.1.6 Generated Draft Predictive Equation 124\u003c\/p\u003e \u003cp\u003e9.6.2 Use Gosset to Select Additional Data Samples 125\u003c\/p\u003e \u003cp\u003e9.6.2.1 Example Gosset Session: User Input to Select Next Points 125\u003c\/p\u003e \u003cp\u003e9.6.2.2 Example Gosset Session: How We Chose User Input 126\u003c\/p\u003e \u003cp\u003e9.6.2.3 Example Gosset Session: User Input Along with Gosset Output 128\u003c\/p\u003e \u003cp\u003e9.6.2.4 Example Gosset Session: Convert the Gosset Design to Operator Values 131\u003c\/p\u003e \u003cp\u003e9.6.2.5 Results of Example Gosset Session: Operator Plots of Total Experiment Plan 132\u003c\/p\u003e \u003cp\u003e9.6.2.6 Execute Stage Two of the Experiment Plan: User Plus Gosset Sample Points 132\u003c\/p\u003e \u003cp\u003e9.7 Use Gosset to Analyze Results 133\u003c\/p\u003e \u003cp\u003e9.8 Other Options and Features of Gosset 133\u003c\/p\u003e \u003cp\u003e9.9 Using Gosset to Find Local Extrema in a Function of Several Variables 134\u003c\/p\u003e \u003cp\u003e9.10 Summary 137\u003c\/p\u003e \u003cp\u003eFurther Reading 137\u003c\/p\u003e \u003cp\u003eHomework 137\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Analyzing Measurement Uncertainty 143\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Clarifying Uncertainty Analysis 143\u003c\/p\u003e \u003cp\u003e10.1.1 Distinguish Error and Uncertainty 144\u003c\/p\u003e \u003cp\u003e10.1.1.1 Single-Sample vs. Multiple-Sample 145\u003c\/p\u003e \u003cp\u003e10.1.2 Uncertainty as a Diagnostic Tool 146\u003c\/p\u003e \u003cp\u003e10.1.2.1 What Can Uncertainty Analysis Tell You? 146\u003c\/p\u003e \u003cp\u003e10.1.2.2 What is Uncertainty Analysis Good For? 148\u003c\/p\u003e \u003cp\u003e10.1.2.3 Uncertainty Analysis Can Redirect a Poorly Conceived Experiment 148\u003c\/p\u003e \u003cp\u003e10.1.2.4 Uncertainty Analysis Improves the Quality of Your Work 148\u003c\/p\u003e \u003cp\u003e10.1.2.5 Slow Sampling and “Randomness” 149\u003c\/p\u003e \u003cp\u003e10.1.2.6 Uncertainty Analysis Makes Results Believable 150\u003c\/p\u003e \u003cp\u003e10.1.3 Uncertainty Analysis Aids Management Decision-Making 150\u003c\/p\u003e \u003cp\u003e10.1.3.1 Management’s Task: Dealing with Warranty Issues 150\u003c\/p\u003e \u003cp\u003e10.1.4 The Design Group’s Task: Setting Tolerances on Performance Test Repeatability 152\u003c\/p\u003e \u003cp\u003e10.1.5 The Performance Test Group’s Task: Setting the Tolerances on Measurements 152\u003c\/p\u003e \u003cp\u003e10.2 Definitions 153\u003c\/p\u003e \u003cp\u003e10.2.1 True Value 153\u003c\/p\u003e \u003cp\u003e10.2.2 Corrected Value 153\u003c\/p\u003e \u003cp\u003e10.2.3 Data Reduction Program 153\u003c\/p\u003e \u003cp\u003e10.2.4 Accuracy 153\u003c\/p\u003e \u003cp\u003e10.2.5 Error 154\u003c\/p\u003e \u003cp\u003e10.2.6 XXXX Error 154\u003c\/p\u003e \u003cp\u003e10.2.7 Fixed Error 154\u003c\/p\u003e \u003cp\u003e10.2.8 Residual Fixed Error 154\u003c\/p\u003e \u003cp\u003e10.2.9 Random Error 154\u003c\/p\u003e \u003cp\u003e10.2.10 Variable (but Deterministic) Error 155\u003c\/p\u003e \u003cp\u003e10.2.11 Uncertainty 155\u003c\/p\u003e \u003cp\u003e10.2.12 Odds 155\u003c\/p\u003e \u003cp\u003e10.2.13 Absolute Uncertainty 155\u003c\/p\u003e \u003cp\u003e10.2.14 Relative Uncertainty 155\u003c\/p\u003e \u003cp\u003e10.3 The Sources and Types of Errors 156\u003c\/p\u003e \u003cp\u003e10.3.1 Types of Errors: Fixed, Random, and Variable 156\u003c\/p\u003e \u003cp\u003e10.3.2 Sources of Errors: The Measurement Chain 156\u003c\/p\u003e \u003cp\u003e10.3.2.1 The Undisturbed Value 158\u003c\/p\u003e \u003cp\u003e10.3.2.2 The Available Value 158\u003c\/p\u003e \u003cp\u003e10.3.2.3 The Achieved Value 158\u003c\/p\u003e \u003cp\u003e10.3.2.4 The Observed Value 159\u003c\/p\u003e \u003cp\u003e10.3.2.5 The Corrected Value 159\u003c\/p\u003e \u003cp\u003e10.3.3 Specifying the True Value 160\u003c\/p\u003e \u003cp\u003e10.3.3.1 If the Achieved Value is Taken as the True Value 160\u003c\/p\u003e \u003cp\u003e10.3.3.2 If the Available Value is Taken as the True Value 163\u003c\/p\u003e \u003cp\u003e10.3.3.3 If the Undisturbed Value is Taken as the True Value 166\u003c\/p\u003e \u003cp\u003e10.3.3.4 If the Mixed Mean Gas Temperature is Taken as the True Value 167\u003c\/p\u003e \u003cp\u003e10.3.4 The Role of the End User 167\u003c\/p\u003e \u003cp\u003e10.3.4.1 The End-Use Equations Implicitly Define the True Value 167\u003c\/p\u003e \u003cp\u003e10.3.5 Calibration 168\u003c\/p\u003e \u003cp\u003e10.4 The Basic Mathematics 170\u003c\/p\u003e \u003cp\u003e10.4.1 The Root-Sum-Squared (RSS) Combination 170\u003c\/p\u003e \u003cp\u003e10.4.2 The Fixed Error in a Measurement 171\u003c\/p\u003e \u003cp\u003e10.4.3 The Random Error in a Measurement 172\u003c\/p\u003e \u003cp\u003e10.4.4 The Uncertainty in a Measurement 173\u003c\/p\u003e \u003cp\u003e10.4.5 The Uncertainty in a Calculated Result 174\u003c\/p\u003e \u003cp\u003e10.4.5.1 The Relative Uncertainty in a Result 176\u003c\/p\u003e \u003cp\u003e10.5 Automating the Uncertainty Analysis 178\u003c\/p\u003e \u003cp\u003e10.5.1 The Mathematical Basis 178\u003c\/p\u003e \u003cp\u003e10.5.2 Example of Uncertainty Analysis by Spreadsheet 179\u003c\/p\u003e \u003cp\u003e10.6 Single-Sample Uncertainty Analysis 181\u003c\/p\u003e \u003cp\u003e10.6.1 Assembling the Necessary Inputs 184\u003c\/p\u003e \u003cp\u003e10.6.2 Calculating the Uncertainty in the Result 185\u003c\/p\u003e \u003cp\u003e10.6.3 The Three Levels of Uncertainty: Zero\u003csup\u003eth\u003c\/sup\u003e-, First-, and N\u003csup\u003eth\u003c\/sup\u003e-Order 185\u003c\/p\u003e \u003cp\u003e10.6.3.1 Zero\u003csup\u003eth\u003c\/sup\u003e-Order Replication 186\u003c\/p\u003e \u003cp\u003e10.6.3.2 First-Order Replication 187\u003c\/p\u003e \u003cp\u003e10.6.3.3 N\u003csup\u003eth\u003c\/sup\u003e-Order Replication 188\u003c\/p\u003e \u003cp\u003e10.6.4 Fractional-Order Replication for Special Cases 188\u003c\/p\u003e \u003cp\u003e10.6.5 Summary of Single-Sample Uncertainty Levels 189\u003c\/p\u003e \u003cp\u003e10.6.5.1 Zero\u003csup\u003eth\u003c\/sup\u003e-Order 189\u003c\/p\u003e \u003cp\u003e10.6.5.2 First-Order 190\u003c\/p\u003e \u003cp\u003e10.6.5.3 N\u003csup\u003eth\u003c\/sup\u003e-Order 190\u003c\/p\u003e \u003cp\u003eReferences 190\u003c\/p\u003e \u003cp\u003eFurther Reading 191\u003c\/p\u003e \u003cp\u003eHomework 191\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Using Uncertainty Analysis in Planning and Execution 197\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Using Uncertainty Analysis in Planning 197\u003c\/p\u003e \u003cp\u003e11.1.1 The Physical Situation and Energy Analysis 198\u003c\/p\u003e \u003cp\u003e11.1.2 The Steady‐State Method 199\u003c\/p\u003e \u003cp\u003e11.1.3 The Transient Method 200\u003c\/p\u003e \u003cp\u003e11.1.4 Reflecting on Assumptions Made During DRE Derivations 201\u003c\/p\u003e \u003cp\u003e11.2 Perform Uncertainty Analysis on the DREs 202\u003c\/p\u003e \u003cp\u003e11.2.1 Uncertainty Analysis: General Form 202\u003c\/p\u003e \u003cp\u003e11.2.2 Uncertainty Analysis of the Steady‐State Method 203\u003c\/p\u003e \u003cp\u003e11.2.3 Uncertainty Analysis – Transient Method 204\u003c\/p\u003e \u003cp\u003e11.2.4 Compare the Results of Uncertainty Analysis of the Methods 205\u003c\/p\u003e \u003cp\u003e11.2.5 What Does the Calculated Uncertainty Interval Mean? 206\u003c\/p\u003e \u003cp\u003e11.2.6 Cross‐Checking the Experiment 207\u003c\/p\u003e \u003cp\u003e11.2.7 Conclusions 207\u003c\/p\u003e \u003cp\u003e11.3 Using Uncertainty Analysis in Selecting Instruments 208\u003c\/p\u003e \u003cp\u003e11.4 Using Uncertainty Analysis in Debugging an Experiment 209\u003c\/p\u003e \u003cp\u003e11.4.1 Handling Overall Scatter 209\u003c\/p\u003e \u003cp\u003e11.4.2 Sources of Scatter 210\u003c\/p\u003e \u003cp\u003e11.4.3 Advancing Toward Calibration 211\u003c\/p\u003e \u003cp\u003e11.4.4 Selecting Thresholds 212\u003c\/p\u003e \u003cp\u003e11.4.5 Iterating Analysis 212\u003c\/p\u003e \u003cp\u003e11.4.6 Rechecking Situational Uncertainty 212\u003c\/p\u003e \u003cp\u003e11.5 Reporting the Uncertainties in an Experiment 213\u003c\/p\u003e \u003cp\u003e11.5.1 Progress in Uncertainty Reporting 214\u003c\/p\u003e \u003cp\u003e11.6 Multiple‐Sample Uncertainty Analysis 214\u003c\/p\u003e \u003cp\u003e11.6.1 Revisiting Single‐Sample and Multiple‐Sample Uncertainty Analysis 214\u003c\/p\u003e \u003cp\u003e11.6.2 Examples of Multiple‐Sample Uncertainty Analysis 215\u003c\/p\u003e \u003cp\u003e11.6.3 Fixed Error and Random Error 216\u003c\/p\u003e \u003cp\u003e11.7 Coordinate with Uncertainty Analysis Standards 216\u003c\/p\u003e \u003cp\u003e11.7.1 Describing Fixed and Random Errors in a Measurement 217\u003c\/p\u003e \u003cp\u003e11.7.2 The Bias Limit 217\u003c\/p\u003e \u003cp\u003e11.7.2.1 Fossilization 218\u003c\/p\u003e \u003cp\u003e11.7.2.2 Bias Limits 218\u003c\/p\u003e \u003cp\u003e11.7.3 The Precision Index 219\u003c\/p\u003e \u003cp\u003e11.7.4 The Number of Degrees of Freedom 220\u003c\/p\u003e \u003cp\u003e11.8 Describing the Overall Uncertainty in a Single Measurement 220\u003c\/p\u003e \u003cp\u003e11.8.1 Adjusting for a Single Measurement 220\u003c\/p\u003e \u003cp\u003e11.8.2 Describing the Overall Uncertainty in a Result 221\u003c\/p\u003e \u003cp\u003e11.8.3 Adding the Overall Uncertainty to Predictive Models 222\u003c\/p\u003e \u003cp\u003e11.9 Additional Statistical Tools and Elements 222\u003c\/p\u003e \u003cp\u003e11.9.1 Pooled Variance 222\u003c\/p\u003e \u003cp\u003e11.9.1.1 Student’s t‐Distribution – Pooled Examples 223\u003c\/p\u003e \u003cp\u003e11.9.2 Estimating the Standard Deviation of a Population from the Standard Deviation of a Small Sample: The Chi‐Squared χ2 Distribution 223\u003c\/p\u003e \u003cp\u003eReferences 225\u003c\/p\u003e \u003cp\u003eHomework 226\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Debugging an Experiment, Shakedown, and Validation 231\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 231\u003c\/p\u003e \u003cp\u003e12.2 Classes of Error 231\u003c\/p\u003e \u003cp\u003e12.3 Using Time-Series Analysis in Debugging 232\u003c\/p\u003e \u003cp\u003e12.4 Examples 232\u003c\/p\u003e \u003cp\u003e12.4.1 Gas Temperature Measurement 232\u003c\/p\u003e \u003cp\u003e12.4.2 Calibration of a Strain Gauge 233\u003c\/p\u003e \u003cp\u003e12.4.3 Lessons Learned from Examples 234\u003c\/p\u003e \u003cp\u003e12.5 Process Unsteadiness 234\u003c\/p\u003e \u003cp\u003e12.6 The Effect of Time-Constant Mismatching 235\u003c\/p\u003e \u003cp\u003e12.7 Using Uncertainty Analysis in Debugging an Experiment 236\u003c\/p\u003e \u003cp\u003e12.7.1 Calibration and Repeatability 236\u003c\/p\u003e \u003cp\u003e12.7.2 Stability and Baselining 238\u003c\/p\u003e \u003cp\u003e12.8 Debugging the Experiment via the Data Interpretation Program 239\u003c\/p\u003e \u003cp\u003e12.8.1 Debug the Experiment via the DIP 239\u003c\/p\u003e \u003cp\u003e12.8.2 Debug the Interface of the DIP 239\u003c\/p\u003e \u003cp\u003e12.8.3 Debug Routines in the DIP 240\u003c\/p\u003e \u003cp\u003e12.9 Situational Uncertainty 241\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Trimming Uncertainty 243\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Focusing on the Goal 243\u003c\/p\u003e \u003cp\u003e13.2 A Motivating Question for Industrial Production 243\u003c\/p\u003e \u003cp\u003e13.2.1 Agreed Motivating Questions for Industrial Example 244\u003c\/p\u003e \u003cp\u003e13.2.2 Quick Answers to Motivating Questions 244\u003c\/p\u003e \u003cp\u003e13.2.3 Challenge: Precheck Analysis and Answers 245\u003c\/p\u003e \u003cp\u003e13.3 Plackett–Burman 12-Run Results and Motivating Question #3 245\u003c\/p\u003e \u003cp\u003e13.4 PB 12-Run Results and Motivating Question #1 247\u003c\/p\u003e \u003cp\u003e13.4.1 Building a Predictive Model Equation from R-Language Linear Model 248\u003c\/p\u003e \u003cp\u003e13.4.2 Parsing the Dual Predictive Model Equation 249\u003c\/p\u003e \u003cp\u003e13.4.3 Uncertainty of the Intercept in the Dual Predictive Model Equation 250\u003c\/p\u003e \u003cp\u003e13.4.4 Mapping an Answer to Motivating Question #1 251\u003c\/p\u003e \u003cp\u003e13.4.5 Tentative Answers to Motivating Question #1 252\u003c\/p\u003e \u003cp\u003e13.5 Uncertainty Analysis of Dual Predictive Model and Motivating Question #2 252\u003c\/p\u003e \u003cp\u003e13.5.1 Uncertainty of the Constant in the Dual Predictive Model Equation 252\u003c\/p\u003e \u003cp\u003e13.5.2 Uncertainty of Other Factors in the Dual Predictive Model Equation 253\u003c\/p\u003e \u003cp\u003e13.5.3 Include All Coefficient Uncertainties in the Dual Predictive Model Equation 254\u003c\/p\u003e \u003cp\u003e13.5.4 Overall Uncertainty from All Factors in the Predictive Model Equation 254\u003c\/p\u003e \u003cp\u003e13.5.5 Improved Tentative Answers to Motivating Questions, Including Uncertainties 256\u003c\/p\u003e \u003cp\u003e13.5.6 Search for Improved Predictive Models 256\u003c\/p\u003e \u003cp\u003e13.6 The PB 12-Run Results and Individual Machine Models 256\u003c\/p\u003e \u003cp\u003e13.6.1 Individual Machine Predictive Model Equations 258\u003c\/p\u003e \u003cp\u003e13.6.2 Uncertainty of the Intercept in the Individual Predictive Model Equations 258\u003c\/p\u003e \u003cp\u003e13.6.3 Uncertainty of the Constant in the Individual Predictive Model Equations 259\u003c\/p\u003e \u003cp\u003e13.6.4 Uncertainty of Other Factors in the Individual Predictive Model Equation 259\u003c\/p\u003e \u003cp\u003e13.6.4.1 Uncertainties of Machine 1 259\u003c\/p\u003e \u003cp\u003e13.6.4.2 Uncertainties of Machine 2 260\u003c\/p\u003e \u003cp\u003e13.6.4.3 Including Instrument and Measurement Uncertainties 260\u003c\/p\u003e \u003cp\u003e13.6.5 Include All Coefficient Uncertainties in the Individual Predictive Model Equations 260\u003c\/p\u003e \u003cp\u003e13.6.6 Overall Uncertainty from All Factors in the Individual Predictive Model Equations 261\u003c\/p\u003e \u003cp\u003e13.6.7 Quick Overview of Individual Machine Performance Over the Operating Map 262\u003c\/p\u003e \u003cp\u003e13.7 Final Answers to All Motivating Questions for the PB Example Experiment 263\u003c\/p\u003e \u003cp\u003e13.7.1 Answers to Motivating Question #1 263\u003c\/p\u003e \u003cp\u003e13.7.2 Answers to Motivating Question #2 263\u003c\/p\u003e \u003cp\u003e13.7.3 Answers to Motivating Question #3 (Expanded from Section 13.3) 263\u003c\/p\u003e \u003cp\u003e13.7.4 Answers to Motivating Question #4 264\u003c\/p\u003e \u003cp\u003e13.7.5 Other Recommendations (to Our Client) 264\u003c\/p\u003e \u003cp\u003e13.8 Conclusions 265\u003c\/p\u003e \u003cp\u003eHomework 266\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Documenting the Experiment: Report Writing 269\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 The Logbook 269\u003c\/p\u003e \u003cp\u003e14.2 Report Writing 269\u003c\/p\u003e \u003cp\u003e14.2.1 Organization of the Reports 270\u003c\/p\u003e \u003cp\u003e14.2.2 Who Reads What? 270\u003c\/p\u003e \u003cp\u003e14.2.3 Picking a Viewpoint 271\u003c\/p\u003e \u003cp\u003e14.2.4 What Goes Where? 271\u003c\/p\u003e \u003cp\u003e14.2.4.1 What Goes in the Abstract? 272\u003c\/p\u003e \u003cp\u003e14.2.4.2 What Goes in the Foreword? 272\u003c\/p\u003e \u003cp\u003e14.2.4.3 What Goes in the Objective? 273\u003c\/p\u003e \u003cp\u003e14.2.4.4 What Goes in the Results and Conclusions? 273\u003c\/p\u003e \u003cp\u003e14.2.4.5 What Goes in the Discussion? 274\u003c\/p\u003e \u003cp\u003e14.2.4.6 References 274\u003c\/p\u003e \u003cp\u003e14.2.4.7 Figures 275\u003c\/p\u003e \u003cp\u003e14.2.4.8 Tables 276\u003c\/p\u003e \u003cp\u003e14.2.4.9 Appendices 276\u003c\/p\u003e \u003cp\u003e14.2.5 The Mechanics of Report Writing 276\u003c\/p\u003e \u003cp\u003e14.2.6 Clear Language Versus “JARGON” 277\u003c\/p\u003e \u003cp\u003ePanel 14.1 The Turbo-Encabulator 278\u003c\/p\u003e \u003cp\u003e14.2.7 “Gobbledygook”: Structural Jargon 279\u003c\/p\u003e \u003cp\u003ePanel 14.2 U.S. Code, Title 18, No. 793 279\u003c\/p\u003e \u003cp\u003e14.2.8 Quantitative Writing 281\u003c\/p\u003e \u003cp\u003e14.2.8.1 Substantive Versus Descriptive Writing 281\u003c\/p\u003e \u003cp\u003ePanel 14.3 The Descriptive Bank Statement 281\u003c\/p\u003e \u003cp\u003e14.2.8.2 Zero-Information Statements 281\u003c\/p\u003e \u003cp\u003e14.2.8.3 Change 282\u003c\/p\u003e \u003cp\u003e14.3 International Organization for Standardization, ISO 9000 and other Standards 282\u003c\/p\u003e \u003cp\u003e14.4 Never Forget. Always Remember 282\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix A: Distributing Variation and Pooled Variance 283\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA.1 Inescapable Distributions 283\u003c\/p\u003e \u003cp\u003eA.1.1 The Normal Distribution for Samples of Infinite Size 283\u003c\/p\u003e \u003cp\u003eA.1.2 Adjust Normal Distributions with Few Data: The Student’s t-Distribution 283\u003c\/p\u003e \u003cp\u003eA.2 Other Common Distributions 286\u003c\/p\u003e \u003cp\u003eA.3 Pooled Variance (Advanced Topic) 286\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix B: Illustrative Tables for Statistical Design 289\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eB.1 Useful Tables for Statistical Design of Experiments 289\u003c\/p\u003e \u003cp\u003eB.1.1 Ready-made Ordering for Randomized Trials 289\u003c\/p\u003e \u003cp\u003eB.1.2 Exhausting Sets of Two-Level Factorial Designs (≤ Five Factors) 289\u003c\/p\u003e \u003cp\u003eB.2 The Plackett–Burman (PB) Screening Designs 289\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix C: Hand Analysis of a Two-Level Factorial Design 293\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eC.1 The General Two-Level Factorial Design 293\u003c\/p\u003e \u003cp\u003eC.2 Estimating the Significance of the Apparent Factor Effects 298\u003c\/p\u003e \u003cp\u003eC.3 Hand Analysis of a Plackett–Burman (PB) 12-Run Design 299\u003c\/p\u003e \u003cp\u003eC.4 Illustrative Practice Example for the PB 12-Run Pattern 302\u003c\/p\u003e \u003cp\u003eC.4.1 Assignment: Find Factor Effects and the Linear Coefficients Absent Noise 302\u003c\/p\u003e \u003cp\u003eC.4.2 Assignment: Find Factor Effects and the Linear Coefficients with Noise 303\u003c\/p\u003e \u003cp\u003eC.5 Answer Key: Compare Your Hand Calculations 303\u003c\/p\u003e \u003cp\u003eC.5.1 Expected Results Absent Noise (compare C.4.1) 303\u003c\/p\u003e \u003cp\u003eC.5.2 Expected Results with Random Gaussian Noise (cf. C.4.2) 304\u003c\/p\u003e \u003cp\u003eC.6 Equations for Hand Calculations 305\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix D: Free Recommended Software 307\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eD.1 Instructions to Obtain the R Language for Statistics 307\u003c\/p\u003e \u003cp\u003eD.2 Instructions to Obtain LibreOffice 308\u003c\/p\u003e \u003cp\u003eD.3 Instructions to Obtain Gosset 308\u003c\/p\u003e \u003cp\u003eD.4 Possible Use of RStudio 309\u003c\/p\u003e \u003cp\u003eIndex 311\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":48866401812823,"sku":"9781119532873","price":91.76,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781119532873.jpg?v=1722278471","url":"https:\/\/bookcurl.com\/products\/planning-and-executing-credible-experiments-9781119532873","provider":"Book Curl","version":"1.0","type":"link"}