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<p>Preface to Second Edition xv</p> <p>Preface to First Edition xvii</p> <p>Abbreviations xix</p> <p><b>PART I PRINCIPLES OF STATISTICAL THINKING AND ANALYSIS 1</b></p> <p><b>1 The Role of Statistical Methods in Modern Industry and Services 3</b></p> <p>1.1 The different functional areas in industry and services 3</p> <p>1.2 The quality-productivity dilemma 5</p> <p>1.3 Fire-fighting 6</p> <p>1.4 Inspection of products 7</p> <p>1.5 Process control 7</p> <p>1.6 Quality by design 8</p> <p>1.7 Information quality and practical statistical efficiency 9</p> <p>1.8 Chapter highlights 11</p> <p>1.9 Exercises 12</p> <p><b>2 Analyzing Variability: Descriptive Statistics 13</b></p> <p>2.1 Random phenomena and the structure of observations 13</p> <p>2.2 Accuracy and precision of measurements 17</p> <p>2.3 The population and the sample 18</p> <p>2.4 Descriptive analysis of sample values 19</p> <p>2.5 Prediction intervals 32</p> <p>2.6 Additional techniques of exploratory data analysis 32</p> <p>2.7 Chapter highlights 38</p> <p>2.8 Exercises 38</p> <p><b>3 Probability Models and Distribution Functions 41</b></p> <p>3.1 Basic probability 41</p> <p>3.2 Random variables and their distributions 51</p> <p>3.3 Families of discrete distribution 60</p> <p>3.4 Continuous distributions 69</p> <p>3.5 Joint, marginal and conditional distributions 82</p> <p>3.6 Some multivariate distributions 88</p> <p>3.7 Distribution of order statistics 92</p> <p>3.8 Linear combinations of random variables 94</p> <p>3.9 Large sample approximations 98</p> <p>3.10 Additional distributions of statistics of normal samples 101</p> <p>3.11 Chapter highlights 104</p> <p>3.12 Exercises 105</p> <p><b>4 Statistical Inference and Bootstrapping 113</b></p> <p>4.1 Sampling characteristics of estimators 113</p> <p>4.2 Some methods of point estimation 114</p> <p>4.3 Comparison of sample estimates 120</p> <p>4.4 Confidence intervals 128</p> <p>4.5 Tolerance intervals 132</p> <p>4.6 Testing for normality with probability plots 134</p> <p>4.7 Tests of goodness of fit 137</p> <p>4.8 Bayesian decision procedures 140</p> <p>4.9 Random sampling from reference distributions 148</p> <p>4.10 Bootstrap sampling 150</p> <p>4.11 Bootstrap testing of hypotheses 152</p> <p>4.12 Bootstrap tolerance intervals 161</p> <p>4.13 Non-parametric tests 165</p> <p>4.14 Description of MINITAB macros (available for download from Appendix VI of the book website) 170</p> <p>4.15 Chapter highlights 170</p> <p>4.16 Exercises 171</p> <p><b>5 Variability in Several Dimensions and Regression Models 177</b></p> <p>5.1 Graphical display and analysis 177</p> <p>5.2 Frequency distributions in several dimensions 181</p> <p>5.3 Correlation and regression analysis 185</p> <p>5.4 Multiple regression 192</p> <p>5.5 Partial regression and correlation 198</p> <p>5.6 Multiple linear regression 200</p> <p>5.7 Partial F-tests and the sequential SS 204</p> <p>5.8 Model construction: Step-wise regression 206</p> <p>5.9 Regression diagnostics 209</p> <p>5.10 Quantal response analysis: Logistic regression 211</p> <p>5.11 The analysis of variance: The comparison of means 213</p> <p>5.12 Simultaneous confidence intervals: Multiple comparisons 216</p> <p>5.13 Contingency tables 220</p> <p>5.14 Categorical data analysis 227</p> <p>5.15 Chapter highlights 229</p> <p>5.16 Exercises 230</p> <p><b>PART II ACCEPTANCE SAMPLING 235</b></p> <p><b>6 Sampling for Estimation of Finite Population Quantities 237</b></p> <p>6.1 Sampling and the estimation problem 237</p> <p>6.2 Estimation with simple random samples 241</p> <p>6.3 Estimating the mean with stratified RSWOR 248</p> <p>6.4 Proportional and optimal allocation 249</p> <p>6.5 Prediction models with known covariates 252</p> <p>6.6 Chapter highlights 255</p> <p>6.7 Exercises 256</p> <p><b>7 Sampling Plans for Product Inspection 258</b></p> <p>7.1 General discussion 258</p> <p>7.2 Single-stage sampling plans for attributes 259</p> <p>7.3 Approximate determination of the sampling plan 262</p> <p>7.4 Double-sampling plans for attributes 264</p> <p>7.5 Sequential sampling 267</p> <p>7.6 Acceptance sampling plans for variables 270</p> <p>7.7 Rectifying inspection of lots 272</p> <p>7.8 National and international standards 274</p> <p>7.9 Skip-lot sampling plans for attributes 276</p> <p>7.10 The Deming inspection criterion 278</p> <p>7.11 Published tables for acceptance sampling 279</p> <p>7.12 Chapter highlights 280</p> <p>7.13 Exercises 281</p> <p><b>PART III STATISTICAL PROCESS CONTROL 283</b></p> <p><b>8 Basic Tools and Principles of Process Control 285</b></p> <p>8.1 Basic concepts of statistical process control 285</p> <p>8.2 Driving a process with control charts 294</p> <p>8.3 Setting up a control chart: Process capability studies 298</p> <p>8.4 Process capability indices 300</p> <p>8.5 Seven tools for process control and process improvement 302</p> <p>8.6 Statistical analysis of Pareto charts 305</p> <p>8.7 The Shewhart control charts 308</p> <p>8.8 Chapter highlights 316</p> <p>8.9 Exercises 316</p> <p><b>9 Advanced Methods of Statistical Process Control 319</b></p> <p>9.1 Tests of randomness 319</p> <p>9.2 Modified Shewhart control charts for X 325</p> <p>9.3 The size and frequency of sampling for Shewhart control charts 328</p> <p>9.4 Cumulative sum control charts 330</p> <p>9.5 Bayesian detection 342</p> <p>9.6 Process tracking 346</p> <p>9.7 Automatic process control 354</p> <p>9.8 Chapter highlights 356</p> <p>9.9 Exercises 357</p> <p><b>10 Multivariate Statistical Process Control 361</b></p> <p>10.1 Introduction 361</p> <p>10.2 A review of multivariate data analysis 365</p> <p>10.3 Multivariate process capability indices 367</p> <p>10.4 Advanced applications of multivariate control charts 370</p> <p>10.5 Multivariate tolerance specifications 374</p> <p>10.6 Chapter highlights 376</p> <p>10.7 Exercises 377</p> <p><b>PART IV DESIGN AND ANALYSIS OF EXPERIMENTS 379</b></p> <p><b>11 Classical Design and Analysis of Experiments 381</b></p> <p>11.1 Basic steps and guiding principles 381</p> <p>11.2 Blocking and randomization 385</p> <p>11.3 Additive and non-additive linear models 385</p> <p>11.4 The analysis of randomized complete block designs 387</p> <p>11.5 Balanced incomplete block designs 394</p> <p>11.6 Latin square design 397</p> <p>11.7 Full factorial experiments 402</p> <p>11.8 Blocking and fractional replications of 2m factorial designs 425</p> <p>11.9 Exploration of response surfaces 430</p> <p>11.10 Chapter highlights 441</p> <p>11.11 Exercises 442</p> <p><b>12 Quality by Design 446</b></p> <p>12.1 Off-line quality control, parameter design and the Taguchi method 447</p> <p>12.2 The effects of non-linearity 452</p> <p>12.3 Taguchi’s designs 456</p> <p>12.4 Quality by design in the pharmaceutical industry 458</p> <p>12.5 Tolerance designs 462</p> <p>12.6 More case studies 467</p> <p>12.7 Chapter highlights 474</p> <p>12.8 Exercises 474</p> <p><b>13 Computer Experiments 477</b></p> <p>13.1 Introduction to computer experiments 477</p> <p>13.2 Designing computer experiments 481</p> <p>13.3 Analyzing computer experiments 483</p> <p>13.4 Stochastic emulators 488</p> <p>13.5 Integrating physical and computer experiments 491</p> <p>13.6 Chapter highlights 492</p> <p>13.7 Exercises 492</p> <p><b>PART V RELIABILITY AND SURVIVAL ANALYSIS 495</b></p> <p><b>14 Reliability Analysis 497</b></p> <p>14.1 Basic notions 498</p> <p>14.2 System reliability 500</p> <p>14.3 Availability of repairable systems 503</p> <p>14.4 Types of observations on TTF 509</p> <p>14.5 Graphical analysis of life data 510</p> <p>14.6 Non-parametric estimation of reliability 513</p> <p>14.7 Estimation of life characteristics 514</p> <p>14.8 Reliability demonstration 520</p> <p>14.9 Accelerated life testing 528</p> <p>14.10 Burn-in procedures 529</p> <p>14.11 Chapter highlights 530</p> <p>14.12 Exercises 531</p> <p><b>15 Bayesian Reliability Estimation and Prediction 534</b></p> <p>15.1 Prior and posterior distributions 534</p> <p>15.2 Loss functions and Bayes estimators 537</p> <p>15.3 Bayesian credibility and prediction intervals 539</p> <p>15.4 Credibility intervals for the asymptotic availability of repairable systems: The exponential case 542</p> <p>15.5 Empirical Bayes method 543</p> <p>15.6 Chapter highlights 545</p> <p>15.7 Exercises 545</p> <p>List of R Packages 547</p> <p>References and Further Reading 549</p> <p>Author Index 555</p> <p>Subject Index 557</p> <p>Also available on book’s website: <a href="http://www.wiley.com/go/modern_industrial_statistics">www.wiley.com/go/modern_industrial_statistics</a></p> <p>Appendix I: An Introduction to R by Stefano Iacus</p> <p>Appendix II: Basic MINITAB Commands and a Review of Matrix Algebra for Statistics</p> <p>Appendix III: mistat Manual (mistat.pdf) and List of R Scripts, by Chapter (R_scripts.zip)</p> <p>Appendix IV: Source Version of mistat Package (mistat_1.0.tar.gz), also available on the</p> <p>Comprehensive R Archive Network (CRAN) Website.</p> <p>Appendix V: Data Sets as csv Files</p> <p>Appendix VI: MINITAB Macros</p> <p>Appendix VII: JMP Scripts by Ian Cox</p> <p>Appendix VIII: Solution Manual</p>

<p>“This book delivers on its promise of providing a theoretical, practical, and computer-based approach to industrial statistics."  (<i>Journal of Quality Technology</i>, 1 October 2014)</p>

<b>RON S. KENETT</b>, The KPA Group, Israel, University of Turin, Italy and NYU Center for Risk Engineering, New York, USA<br /><br /><b>SHELEMYAHU ZACKS</b>, Binghamton University, Binghamton, USA<br /><br />With contributions from <b>DANIELE AMBERTI</b>, Turin, Italy

<p>Fully revised and updated, this book combines a theoretical background with examples and references to R, MINITAB and JMP, enabling practitioners to find state-of-the-art material on both foundation and implementation tools to support their work. Topics addressed include computer-intensive data analysis, acceptance sampling, univariate and multivariate statistical process control, design of experiments, quality by design, and reliability using classical and Bayesian methods. The book can be used for workshops or courses on acceptance sampling, statistical process control, design of experiments, and reliability.</p> <p>Graduate and post-graduate students in the areas of statistical quality and engineering, as well as industrial statisticians, researchers and practitioners in these fields will all benefit from the comprehensive combination of theoretical and practical information provided in this single volume.</p> <p><i>Modern Industrial Statistics: With applications in R, MINITAB and JMP:</i></p> <ul> <li>Combines a practical approach with theoretical foundations and computational support.</li> <li>Provides examples in R using a dedicated package called MISTAT, and also refers to MINITAB and JMP.</li> <li>Includes exercises at the end of each chapter to aid learning and test knowledge.</li> <li>Provides over 40 data sets representing real-life case studies.</li> <li>Is complemented by a comprehensive website providing an introduction to R, and installations of JMP scripts and MINITAB macros, including effective tutorials with introductory material: <b>www.wiley.com/go/modern_industrial_statistics</b><b>.</b></li> </ul>

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