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Modern Industrial Statistics: with applications in R, MINITAB and JMP, 2nd Edition

ISBN: 978-1-118-76369-8
592 pages
December 2013
Modern Industrial Statistics: with applications in R, MINITAB and JMP, 2nd Edition (1118763696) cover image

Description

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.

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.

Modern Industrial Statistics: With applications in R, MINITAB and JMP:

  • Combines a practical approach with theoretical foundations and computational support.
  • Provides examples in R using a dedicated package called MISTAT, and also refers to MINITAB and JMP.
  • Includes exercises at the end of each chapter to aid learning and test knowledge.
  • Provides over 40 data sets representing real-life case studies.
  • 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: www.wiley.com/go/modern_industrial_statistics.
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Table of Contents

Preface to Second Edition xv

Preface to First Edition xvii

Abbreviations xix

PART I PRINCIPLES OF STATISTICAL THINKING AND ANALYSIS 1

1 The Role of Statistical Methods in Modern Industry and Services 3

1.1 The different functional areas in industry and services 3

1.2 The quality-productivity dilemma 5

1.3 Fire-fighting 6

1.4 Inspection of products 7

1.5 Process control 7

1.6 Quality by design 8

1.7 Information quality and practical statistical efficiency 9

1.8 Chapter highlights 11

1.9 Exercises 12

2 Analyzing Variability: Descriptive Statistics 13

2.1 Random phenomena and the structure of observations 13

2.2 Accuracy and precision of measurements 17

2.3 The population and the sample 18

2.4 Descriptive analysis of sample values 19

2.5 Prediction intervals 32

2.6 Additional techniques of exploratory data analysis 32

2.7 Chapter highlights 38

2.8 Exercises 38

3 Probability Models and Distribution Functions 41

3.1 Basic probability 41

3.2 Random variables and their distributions 51

3.3 Families of discrete distribution 60

3.4 Continuous distributions 69

3.5 Joint, marginal and conditional distributions 82

3.6 Some multivariate distributions 88

3.7 Distribution of order statistics 92

3.8 Linear combinations of random variables 94

3.9 Large sample approximations 98

3.10 Additional distributions of statistics of normal samples 101

3.11 Chapter highlights 104

3.12 Exercises 105

4 Statistical Inference and Bootstrapping 113

4.1 Sampling characteristics of estimators 113

4.2 Some methods of point estimation 114

4.3 Comparison of sample estimates 120

4.4 Confidence intervals 128

4.5 Tolerance intervals 132

4.6 Testing for normality with probability plots 134

4.7 Tests of goodness of fit 137

4.8 Bayesian decision procedures 140

4.9 Random sampling from reference distributions 148

4.10 Bootstrap sampling 150

4.11 Bootstrap testing of hypotheses 152

4.12 Bootstrap tolerance intervals 161

4.13 Non-parametric tests 165

4.14 Description of MINITAB macros (available for download from Appendix VI of the book website) 170

4.15 Chapter highlights 170

4.16 Exercises 171

5 Variability in Several Dimensions and Regression Models 177

5.1 Graphical display and analysis 177

5.2 Frequency distributions in several dimensions 181

5.3 Correlation and regression analysis 185

5.4 Multiple regression 192

5.5 Partial regression and correlation 198

5.6 Multiple linear regression 200

5.7 Partial F-tests and the sequential SS 204

5.8 Model construction: Step-wise regression 206

5.9 Regression diagnostics 209

5.10 Quantal response analysis: Logistic regression 211

5.11 The analysis of variance: The comparison of means 213

5.12 Simultaneous confidence intervals: Multiple comparisons 216

5.13 Contingency tables 220

5.14 Categorical data analysis 227

5.15 Chapter highlights 229

5.16 Exercises 230

PART II ACCEPTANCE SAMPLING 235

6 Sampling for Estimation of Finite Population Quantities 237

6.1 Sampling and the estimation problem 237

6.2 Estimation with simple random samples 241

6.3 Estimating the mean with stratified RSWOR 248

6.4 Proportional and optimal allocation 249

6.5 Prediction models with known covariates 252

6.6 Chapter highlights 255

6.7 Exercises 256

7 Sampling Plans for Product Inspection 258

7.1 General discussion 258

7.2 Single-stage sampling plans for attributes 259

7.3 Approximate determination of the sampling plan 262

7.4 Double-sampling plans for attributes 264

7.5 Sequential sampling 267

7.6 Acceptance sampling plans for variables 270

7.7 Rectifying inspection of lots 272

7.8 National and international standards 274

7.9 Skip-lot sampling plans for attributes 276

7.10 The Deming inspection criterion 278

7.11 Published tables for acceptance sampling 279

7.12 Chapter highlights 280

7.13 Exercises 281

PART III STATISTICAL PROCESS CONTROL 283

8 Basic Tools and Principles of Process Control 285

8.1 Basic concepts of statistical process control 285

8.2 Driving a process with control charts 294

8.3 Setting up a control chart: Process capability studies 298

8.4 Process capability indices 300

8.5 Seven tools for process control and process improvement 302

8.6 Statistical analysis of Pareto charts 305

8.7 The Shewhart control charts 308

8.8 Chapter highlights 316

8.9 Exercises 316

9 Advanced Methods of Statistical Process Control 319

9.1 Tests of randomness 319

9.2 Modified Shewhart control charts for X 325

9.3 The size and frequency of sampling for Shewhart control charts 328

9.4 Cumulative sum control charts 330

9.5 Bayesian detection 342

9.6 Process tracking 346

9.7 Automatic process control 354

9.8 Chapter highlights 356

9.9 Exercises 357

10 Multivariate Statistical Process Control 361

10.1 Introduction 361

10.2 A review of multivariate data analysis 365

10.3 Multivariate process capability indices 367

10.4 Advanced applications of multivariate control charts 370

10.5 Multivariate tolerance specifications 374

10.6 Chapter highlights 376

10.7 Exercises 377

PART IV DESIGN AND ANALYSIS OF EXPERIMENTS 379

11 Classical Design and Analysis of Experiments 381

11.1 Basic steps and guiding principles 381

11.2 Blocking and randomization 385

11.3 Additive and non-additive linear models 385

11.4 The analysis of randomized complete block designs 387

11.5 Balanced incomplete block designs 394

11.6 Latin square design 397

11.7 Full factorial experiments 402

11.8 Blocking and fractional replications of 2m factorial designs 425

11.9 Exploration of response surfaces 430

11.10 Chapter highlights 441

11.11 Exercises 442

12 Quality by Design 446

12.1 Off-line quality control, parameter design and the Taguchi method 447

12.2 The effects of non-linearity 452

12.3 Taguchi’s designs 456

12.4 Quality by design in the pharmaceutical industry 458

12.5 Tolerance designs 462

12.6 More case studies 467

12.7 Chapter highlights 474

12.8 Exercises 474

13 Computer Experiments 477

13.1 Introduction to computer experiments 477

13.2 Designing computer experiments 481

13.3 Analyzing computer experiments 483

13.4 Stochastic emulators 488

13.5 Integrating physical and computer experiments 491

13.6 Chapter highlights 492

13.7 Exercises 492

PART V RELIABILITY AND SURVIVAL ANALYSIS 495

14 Reliability Analysis 497

14.1 Basic notions 498

14.2 System reliability 500

14.3 Availability of repairable systems 503

14.4 Types of observations on TTF 509

14.5 Graphical analysis of life data 510

14.6 Non-parametric estimation of reliability 513

14.7 Estimation of life characteristics 514

14.8 Reliability demonstration 520

14.9 Accelerated life testing 528

14.10 Burn-in procedures 529

14.11 Chapter highlights 530

14.12 Exercises 531

15 Bayesian Reliability Estimation and Prediction 534

15.1 Prior and posterior distributions 534

15.2 Loss functions and Bayes estimators 537

15.3 Bayesian credibility and prediction intervals 539

15.4 Credibility intervals for the asymptotic availability of repairable systems: The exponential case 542

15.5 Empirical Bayes method 543

15.6 Chapter highlights 545

15.7 Exercises 545

List of R Packages 547

References and Further Reading 549

Author Index 555

Subject Index 557

Also available on book’s website: www.wiley.com/go/modern_industrial_statistics

Appendix I: An Introduction to R by Stefano Iacus

Appendix II: Basic MINITAB Commands and a Review of Matrix Algebra for Statistics

Appendix III: mistat Manual (mistat.pdf) and List of R Scripts, by Chapter (R_scripts.zip)

Appendix IV: Source Version of mistat Package (mistat_1.0.tar.gz), also available on the

Comprehensive R Archive Network (CRAN) Website.

Appendix V: Data Sets as csv Files

Appendix VI: MINITAB Macros

Appendix VII: JMP Scripts by Ian Cox

Appendix VIII: Solution Manual

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Author Information

RON S. KENETT, The KPA Group, Israel, University of Turin, Italy and NYU Center for Risk Engineering, New York, USA

SHELEMYAHU ZACKS, Binghamton University, Binghamton, USA

With contributions from DANIELE AMBERTI, Turin, Italy
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Reviews

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

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