{"product_id":"statistical-control-by-monitoring-and-adjustment-9780470148327","title":"Statistical Control by Monitoring and Adjustment","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003ePraise for the First Edition \"This book... is a significant addition to the literature on statistical practice... should be of considerable interest to those interested in these topics.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePreface xi\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction and Revision of Some Statistical Ideas 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Necessity for Process Control 1\u003c\/p\u003e \u003cp\u003e1.2 SPC and EPC 1\u003c\/p\u003e \u003cp\u003e1.3 Process Monitoring Without a Model 3\u003c\/p\u003e \u003cp\u003e1.4 Detecting a Signal in Noise 4\u003c\/p\u003e \u003cp\u003e1.5 Measurement Data 4\u003c\/p\u003e \u003cp\u003e1.6 Two Important Characteristics of a Probability Distribution 5\u003c\/p\u003e \u003cp\u003e1.7 Normal Distribution 6\u003c\/p\u003e \u003cp\u003e1.8 Normal Distribution Defined by \u003ci\u003eμ \u003c\/i\u003eand \u003ci\u003eσ\u003c\/i\u003e 6\u003c\/p\u003e \u003cp\u003e1.9 Probabilities Associated with Normal Distribution 7\u003c\/p\u003e \u003cp\u003e1.10 Estimating Mean and Standard Deviation from Data 8\u003c\/p\u003e \u003cp\u003e1.11 Combining Estimates of \u003ci\u003eσ\u003c\/i\u003e\u003csup\u003e2\u003c\/sup\u003e 9\u003c\/p\u003e \u003cp\u003e1.12 Data on Frequencies (Events): Poisson Distribution 10\u003c\/p\u003e \u003cp\u003e1.13 Normal Approximation to Poisson Distribution 12\u003c\/p\u003e \u003cp\u003e1.14 Data on Proportion Defective: Binomial Distribution 12\u003c\/p\u003e \u003cp\u003e1.15 Normal Approximation to Binomial Distribution 14\u003c\/p\u003e \u003cp\u003eAppendix 1A: Central Limit Effect 15\u003c\/p\u003e \u003cp\u003eProblems 17\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Standard Control Charts Under Ideal Conditions As a First Approximation 21\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Control Charts for Process Monitoring 21\u003c\/p\u003e \u003cp\u003e2.2 Control Chart for Measurement (Variables) Data 22\u003c\/p\u003e \u003cp\u003e2.3 Shewhart Charts for Sample Average and Range 24\u003c\/p\u003e \u003cp\u003e2.4 Shewhart Chart for Sample Range 26\u003c\/p\u003e \u003cp\u003e2.5 Process Monitoring With Control Charts for Frequencies 29\u003c\/p\u003e \u003cp\u003e2.6 Data on Frequencies (Counts): Poisson Distribution 30\u003c\/p\u003e \u003cp\u003e2.7 Common Causes and Special Causes 34\u003c\/p\u003e \u003cp\u003e2.8 For What Kinds of Data Has the \u003ci\u003ec \u003c\/i\u003eChart Been Used? 36\u003c\/p\u003e \u003cp\u003e2.9 Quality Control Charts for Proportions: \u003ci\u003ep \u003c\/i\u003eChart 37\u003c\/p\u003e \u003cp\u003e2.10 EWMA Chart 40\u003c\/p\u003e \u003cp\u003e2.11 Process Monitoring Using Cumulative Sums 46\u003c\/p\u003e \u003cp\u003e2.12 Specification Limits, Target Accuracy, and Process Capability 53\u003c\/p\u003e \u003cp\u003e2.13 How Successful Process Monitoring can Improve Quality 56\u003c\/p\u003e \u003cp\u003eProblems 57\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 What Can Go Wrong and What Can We Do About It? 61\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 61\u003c\/p\u003e \u003cp\u003e3.2 Measurement Charts 64\u003c\/p\u003e \u003cp\u003e3.3 Need for Time Series Models 65\u003c\/p\u003e \u003cp\u003e3.4 Types of Variation 65\u003c\/p\u003e \u003cp\u003e3.5 Nonstationary Noise 66\u003c\/p\u003e \u003cp\u003e3.6 Values for constants 71\u003c\/p\u003e \u003cp\u003e3.7 Frequencies and Proportions 74\u003c\/p\u003e \u003cp\u003e3.8 Illustration 76\u003c\/p\u003e \u003cp\u003e3.9 Robustness of EWMA 78\u003c\/p\u003e \u003cp\u003eAppendix 3A: Alternative Forms of Relationships for EWMAs 79\u003c\/p\u003e \u003cp\u003eQuestions 79\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Introduction to Forecasting and Process Dynamics 81\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Forecasting with an EWMA 81\u003c\/p\u003e \u003cp\u003e4.2 Forecasting Sales of Dingles 82\u003c\/p\u003e \u003cp\u003e4.3 Pete’s Rule 85\u003c\/p\u003e \u003cp\u003e4.4 Effect of Changing Discount Factor 86\u003c\/p\u003e \u003cp\u003e4.5 Estimating Best Discount Factor 87\u003c\/p\u003e \u003cp\u003e4.6 Standard Deviation of Forecast Errors and Probability Limits for Forecasts 88\u003c\/p\u003e \u003cp\u003e4.7 What to Do If You Do Not Have Enough Data to Estimate \u003ci\u003eθ\u003c\/i\u003e 89\u003c\/p\u003e \u003cp\u003e4.8 Introduction to Process Dynamics and Transfer Function 89\u003c\/p\u003e \u003cp\u003e4.9 Dynamic Systems and Transfer Funtions 90\u003c\/p\u003e \u003cp\u003e4.10 Difference Equations to Represent Dynamic Relations 90\u003c\/p\u003e \u003cp\u003e4.11 Representing Dynamics of Industrial Process 96\u003c\/p\u003e \u003cp\u003e4.12 Transfer Function Models Using Difference Equations 97\u003c\/p\u003e \u003cp\u003e4.13 Stable and Unstable Systems 98\u003c\/p\u003e \u003cp\u003eProblems 100\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Nonstationary Time Series Models for Process Disturbances 103\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Reprise 103\u003c\/p\u003e \u003cp\u003e5.2 Stationary Time Series Model in Which Successive Values are Correlated 104\u003c\/p\u003e \u003cp\u003e5.3 Major Effects of Statistical Dependence: Illustration 105\u003c\/p\u003e \u003cp\u003e5.4 Random Walk 106\u003c\/p\u003e \u003cp\u003e5.5 How to Test a Forecasting Method 107\u003c\/p\u003e \u003cp\u003e5.6 Qualification of EWMA as a Forecast 107\u003c\/p\u003e \u003cp\u003e5.7 Understanding Time Series Behavior with Variogram 110\u003c\/p\u003e \u003cp\u003e5.8 Sticky Innovation Generating Model for Nonstationary Noise 113\u003c\/p\u003e \u003cp\u003e5.9 Robustness of EWMA for Signal Extraction 118\u003c\/p\u003e \u003cp\u003e5.10 Signal Extraction for Disturbance Model Due to Barnard 118\u003c\/p\u003e \u003cp\u003eQuestions 122\u003c\/p\u003e \u003cp\u003eProblems 122\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Repeated-Feedback Adjustment 125\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction to Discrete-Feedback Control 125\u003c\/p\u003e \u003cp\u003e6.2 Inadequacy of NIID Models and Other Stationary Models for Control: Reiteration 125\u003c\/p\u003e \u003cp\u003e6.3 Three Approaches to Repeated-Feedback Adjustment that Lead to Identical Conclusions 126\u003c\/p\u003e \u003cp\u003e6.4 Some History 130\u003c\/p\u003e \u003cp\u003e6.5 Adjustment Chart 132\u003c\/p\u003e \u003cp\u003e6.6 Insensitivity to Choice of \u003ci\u003eG\u003c\/i\u003e 134\u003c\/p\u003e \u003cp\u003e6.7 Compromise Value for \u003ci\u003eG\u003c\/i\u003e 135\u003c\/p\u003e \u003cp\u003e6.8 Using Smaller Value of \u003ci\u003eG \u003c\/i\u003eto Reduce Adjustment Variance \u003ci\u003eσ\u003c\/i\u003e\u003csup\u003e2\u003c\/sup\u003e\u003ci\u003e\u003csub\u003ex\u003c\/sub\u003e \u003c\/i\u003e136\u003c\/p\u003e \u003cp\u003eAppendix 6A: Robustness of Integral Control 137\u003c\/p\u003e \u003cp\u003eAppendix 6B: Effect on Adjustment of Choosing \u003ci\u003eG \u003c\/i\u003eDifferent from \u003ci\u003eλ\u003c\/i\u003e\u003csub\u003e0\u003c\/sub\u003e: Obtaining Equation (6.12) 139\u003c\/p\u003e \u003cp\u003eAppendix 6C: Average Reduction in Mean-Square Error Due to Adjustment for Observations Generated by IMA Model 140\u003c\/p\u003e \u003cp\u003eQuestions 140\u003c\/p\u003e \u003cp\u003eProblems 140\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Periodic Adjustment 143\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 143\u003c\/p\u003e \u003cp\u003e7.2 Periodic Adjustment 143\u003c\/p\u003e \u003cp\u003e7.3 Starting Scheme for Periodic Adjustment 146\u003c\/p\u003e \u003cp\u003e7.4 Numerical Calculations for Bounded Adjustment 146\u003c\/p\u003e \u003cp\u003e7.5 Simple Device for Facilitating Bounded Adjustment 150\u003c\/p\u003e \u003cp\u003e7.6 Bounded Adjustment Seen as Process of Tracking 153\u003c\/p\u003e \u003cp\u003e7.7 Combination of Adjustment and Monitoring 153\u003c\/p\u003e \u003cp\u003e7.8 Bounded Adjustment for Series not Generated by IMA Model 155\u003c\/p\u003e \u003cp\u003eProblems 160\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Control of Process with Inertia 163\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Adjustment Depending on Last Two Output Errors 163\u003c\/p\u003e \u003cp\u003e8.2 Minimum Mean-Square Error Control of Process With First-Order Dynamics 167\u003c\/p\u003e \u003cp\u003e8.3 Schemes with Constrained Adjustment 169\u003c\/p\u003e \u003cp\u003e8.4 PI Schemes with Constrained Adjustment 170\u003c\/p\u003e \u003cp\u003e8.5 Optimal and Near-Optimal Constrained PI Schemes: Choice of \u003ci\u003eP\u003c\/i\u003e 171\u003c\/p\u003e \u003cp\u003e8.6 Choice of \u003ci\u003eG \u003c\/i\u003eFor \u003ci\u003eP \u003c\/i\u003e= 0 and \u003ci\u003eP \u003c\/i\u003e= −0\u003ci\u003e.\u003c\/i\u003e25 172\u003c\/p\u003e \u003cp\u003e8.7 PI Schemes for Process With Dead Time 178\u003c\/p\u003e \u003cp\u003e8.8 Process Monitoring and Process Adjustment 181\u003c\/p\u003e \u003cp\u003e8.9 Feedback Adjustment Applied to Process in Perfect State of Control 182\u003c\/p\u003e \u003cp\u003e8.10 Using Shewhart Chart to Adjust Unstable Process 182\u003c\/p\u003e \u003cp\u003e8.11 Feedforward Control 183\u003c\/p\u003e \u003cp\u003eAppendix 8A: Equivalence of Equations for PI Control 184\u003c\/p\u003e \u003cp\u003eAppendix 8B: Effect of Errors in Adjustment 184\u003c\/p\u003e \u003cp\u003eAppendix 8C: Choices for \u003ci\u003eG \u003c\/i\u003eand \u003ci\u003eP \u003c\/i\u003eto Attain Optimal Constrained PI Control for Various Values of \u003ci\u003eλ\u003c\/i\u003e\u003csub\u003e0\u003c\/sub\u003e and \u003ci\u003eδ\u003c\/i\u003e\u003csub\u003e0\u003c\/sub\u003e with \u003ci\u003ed\u003c\/i\u003e\u003csub\u003e0\u003c\/sub\u003e = 0 and \u003ci\u003ed\u003c\/i\u003e\u003csub\u003e0\u003c\/sub\u003e = 1 185\u003c\/p\u003e \u003cp\u003eQuestions 191\u003c\/p\u003e \u003cp\u003eProblems 191\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Explicit Consideration of Monetary Cost 193\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 193\u003c\/p\u003e \u003cp\u003e9.2 How Often Should You Take Data? 197\u003c\/p\u003e \u003cp\u003e9.3 Choosing Adjustment Schemes Directly in Terms of Costs 203\u003c\/p\u003e \u003cp\u003eAppendix 9A: Functions \u003ci\u003eh(L\/λσ\u003csub\u003ea\u003c\/sub\u003e) \u003c\/i\u003eand \u003ci\u003eq(L\/λσ\u003csub\u003ea\u003c\/sub\u003e) \u003c\/i\u003ein Table 9.1 205\u003c\/p\u003e \u003cp\u003eAppendix 9B: Calculation of Minimum-Cost Schemes 205\u003c\/p\u003e \u003cp\u003eProblems 207\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Cuscore Charts: Looking for Signals in Noise 209\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 209\u003c\/p\u003e \u003cp\u003e10.2 How Are Cuscore Statistics Obtained? 216\u003c\/p\u003e \u003cp\u003e10.3 Efficient Monitoring Charts 219\u003c\/p\u003e \u003cp\u003e10.4 Useful Method for Obtaining Detector When Looking for Signal in Noise Not Necessarily White Noise 221\u003c\/p\u003e \u003cp\u003e10.5 Looking for Single Spike 223\u003c\/p\u003e \u003cp\u003e10.6 Some Time Series Examples 224\u003c\/p\u003e \u003cp\u003eAppendix 10A: Likelihood, Fisher’s Efficient Score, and Cuscore Statistics 227\u003c\/p\u003e \u003cp\u003eAppendix 10B: Useful Procedure for Obtaining Appropriate Cuscore Statistic 230\u003c\/p\u003e \u003cp\u003eAppendix 10C: Detector Series for IMA Model 231\u003c\/p\u003e \u003cp\u003eProblems 231\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Monitoring an Operating Feedback System 235\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Looking for Spike in Disturbance \u003ci\u003ezt \u003c\/i\u003eSubjected to Integral Control 235\u003c\/p\u003e \u003cp\u003e11.2 Looking for Exponential Signal in Disturbance Subject to Integral Control 237\u003c\/p\u003e \u003cp\u003e11.3 Monitoring Process with Inertia Represented by First-Order Dynamics 238\u003c\/p\u003e \u003cp\u003e11.4 Reconstructing Disturbance Pattern 240\u003c\/p\u003e \u003cp\u003eAppendix 11A: Derivation of Equation (11.3) 240\u003c\/p\u003e \u003cp\u003eAppendix 11B: Derivation of Equation (11.10) 242\u003c\/p\u003e \u003cp\u003eAppendix 11C: Derivation of Equation (11.14) 243\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Brief Review of Time Series Analysis 245\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Serial Dependence: Autocorrelation Function and Variogram 245\u003c\/p\u003e \u003cp\u003e12.2 Relation of Autocorrelation Function and Variogram 246\u003c\/p\u003e \u003cp\u003e12.3 Some Time Series Models 247\u003c\/p\u003e \u003cp\u003e12.4 Stationary Models 247\u003c\/p\u003e \u003cp\u003e12.5 Autoregressive Moving-Average Models 250\u003c\/p\u003e \u003cp\u003e12.6 Nonstationary Models 253\u003c\/p\u003e \u003cp\u003e12.7 IMA [or ARIMA(0, 1, 1)] Model 253\u003c\/p\u003e \u003cp\u003e12.8 Modeling Time Series Data 255\u003c\/p\u003e \u003cp\u003e12.9 Model Identification, Model Fitting, and Diagnostic Checking 256\u003c\/p\u003e \u003cp\u003e12.10 Forecasting 261\u003c\/p\u003e \u003cp\u003e12.11 Estimation with Closed-Loop Data 266\u003c\/p\u003e \u003cp\u003e12.12 Conclusion 269\u003c\/p\u003e \u003cp\u003eAppendix 12A: Other Tools for Identification of Time Series Models 269\u003c\/p\u003e \u003cp\u003eAppendix 12B: Estimation of Time Series Parameters 270\u003c\/p\u003e \u003cp\u003eSolutions to Exercises and Problems 273\u003c\/p\u003e \u003cp\u003eReferences and Further Reading 307\u003c\/p\u003e \u003cp\u003eAppendix Three Time Series 321\u003c\/p\u003e \u003cp\u003eIndex 327\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default 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