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Modern Industrial Statistics


STATISTICS IN PRACTICE
Series Advisors
Human and Biological Sciences
Stephen Senn
CRP-Sante,
́ Luxembourg
Earth and Environmental Sciences
Marian Scott
University of Glasgow, UK
Industry, Commerce and Finance
Wolfgang Jank
University of Maryland, USA
Founding Editor
Vic Barnett
Nottingham Trent University, UK


Statistics in Practice is an important international series of texts which provide detailed coverage of
statistical concepts, methods and worked case studies in specific fields of investigation and study.
With sound motivation and many worked practical examples, the books show in down-to-earth
terms how to select and use an appropriate range of statistical techniques in a particular practical
field within each title’s special topic area.
The books provide statistical support for professionals and research workers across a range of
employment fields and research environments. Subject areas covered include medicine and pharmaceutics; industry, finance and commerce; public services; the earth and environmental sciences,
and so on.
The books also provide support to students studying statistical courses applied to the above areas.
The demand for graduates to be equipped for the work environment has led to such courses becoming increasingly prevalent at universities and colleges.
It is our aim to present judiciously chosen and well-written workbooks to meet everyday practical
needs. Feedback of views from readers will be most valuable to monitor the success of this aim.
A complete list of titles in this series appears at the end of the volume.


Modern Industrial Statistics
with applications in R, MINITAB and JMP

Second Edition

RON S. KENETT
Chairman and CEO, the KPA Group, Israel
Research Professor, University of Turin, Italy, and
International Professor, NYU, Center for Risk Engineering, New York, USA

SHELEMYAHU ZACKS
Distinguished Professor,
Binghamton University, Binghamton, USA

With contributions from
DANIELE AMBERTI
Turin, Italy


This edition first published 2014
© 2014 John Wiley & Sons, Ltd
Registered office
John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, United Kingdom
For details of our global editorial offices, for customer services and for information about how to apply for permission to reuse the
copyright material in this book please see our website at www.wiley.com.

The right of the author to be identified as the author of this work has been asserted in accordance with the Copyright, Designs and
Patents Act 1988.
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any
means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by the UK Copyright, Designs and Patents
Act 1988, without the prior permission of the publisher.
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professional advice or other expert assistance is required, the services of a competent professional should be sought.

Library of Congress Cataloging-in-Publication Data
Kenett, Ron.
Modern industrial statistics : with applications in R, MINITAB and JMP / Ron S. Kenett, Shelemyahu
Zacks. – Second edition.
pages cm
Includes bibliographical references and index.
ISBN 978-1-118-45606-4 (cloth)
1. Quality control–Statistical methods. 2. Reliability (Engineering)–Statistical methods. 3. R (Computer
program language) 4. Minitab. 5. JMP (Computer file) I. Zacks, Shelemyahu, 1932- II. Title.
TS156.K42 2014
658.5′ 62–dc23
2013031273
A catalogue record for this book is available from the British Library.
ISBN: 978-1-118-45606-4
Typeset in 9/11pt TimesLTStd by Laserwords Private Limited, Chennai, India
1

2014


To my wife Sima, our children and their children: Yonatan, Alma, Tomer,
Yadin, Aviv and Gili.
RSK
To my wife Hanna, our sons Yuval and David, and their families with love.
SZ
To my wife Nadia, and my family. With a special thought to my mother and
thank you to my father.
DA



Contents

Preface to Second Edition
Preface to First Edition

xv
xvii

Abbreviations

xix

PART I
1

The Role of Statistical Methods in Modern Industry and Services
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
1.9

2

The different functional areas in industry and services
The quality-productivity dilemma
Fire-fighting
Inspection of products
Process control
Quality by design
Information quality and practical statistical efficiency
Chapter highlights
Exercises

1
3
3
5
6
7
7
8
9
11
12

Analyzing Variability: Descriptive Statistics

13

2.1
2.2
2.3
2.4

13
17
18
19
19
23
26
28
32
32
33
34
34
36
38
38

2.5
2.6

2.7
2.8

3

PRINCIPLES OF STATISTICAL THINKING AND ANALYSIS

Random phenomena and the structure of observations
Accuracy and precision of measurements
The population and the sample
Descriptive analysis of sample values
2.4.1 Frequency distributions of discrete random variables
2.4.2 Frequency distributions of continuous random variables
2.4.3 Statistics of the ordered sample
2.4.4 Statistics of location and dispersion
Prediction intervals
Additional techniques of exploratory data analysis
2.6.1 Box and whiskers plot
2.6.2 Quantile plots
2.6.3 Stem-and-leaf diagrams
2.6.4 Robust statistics for location and dispersion
Chapter highlights
Exercises

Probability Models and Distribution Functions

41

3.1

41
41
42
44

Basic probability
3.1.1 Events and sample spaces: Formal presentation of random measurements
3.1.2 Basic rules of operations with events: Unions, intersections
3.1.3 Probabilities of events


viii

4

Contents
3.1.4 Probability functions for random sampling
3.1.5 Conditional probabilities and independence of events
3.1.6 Bayes formula and its application
3.2 Random variables and their distributions
3.2.1 Discrete and continuous distributions
3.2.2 Expected values and moments of distributions
3.2.3 The standard deviation, quantiles, measures of skewness and kurtosis
3.2.4 Moment generating functions
3.3 Families of discrete distribution
3.3.1 The binomial distribution
3.3.2 The hypergeometric distribution
3.3.3 The Poisson distribution
3.3.4 The geometric and negative binomial distributions
3.4 Continuous distributions
3.4.1 The uniform distribution on the interval (a, b), a < b
3.4.2 The normal and log-normal distributions
3.4.3 The exponential distribution
3.4.4 The gamma and Weibull distributions
3.4.5 The Beta distributions
3.5 Joint, marginal and conditional distributions
3.5.1 Joint and marginal distributions
3.5.2 Covariance and correlation
3.5.3 Conditional distributions
3.6 Some multivariate distributions
3.6.1 The multinomial distribution
3.6.2 The multi-hypergeometric distribution
3.6.3 The bivariate normal distribution
3.7 Distribution of order statistics
3.8 Linear combinations of random variables
3.9 Large sample approximations
3.9.1 The law of large numbers
3.9.2 The Central Limit Theorem
3.9.3 Some normal approximations
3.10 Additional distributions of statistics of normal samples
3.10.1 Distribution of the sample variance
3.10.2 The “Student” t-statistic
3.10.3 Distribution of the variance ratio
3.11 Chapter highlights
3.12 Exercises

46
47
49
51
51
55
57
59
60
60
62
65
67
69
69
70
75
77
80
82
82
84
86
88
88
89
90
92
94
98
98
99
99
101
101
102
102
104
105

Statistical Inference and Bootstrapping

113

4.1
4.2

113
114
115
116
118
120
120
122
128

4.3

4.4

Sampling characteristics of estimators
Some methods of point estimation
4.2.1 Moment equation estimators
4.2.2 The method of least squares
4.2.3 Maximum likelihood estimators
Comparison of sample estimates
4.3.1 Basic concepts
4.3.2 Some common one-sample tests of hypotheses
Confidence intervals


Contents ix

4.5
4.6
4.7

4.8

4.9
4.10

4.11

4.12

4.13

4.14
4.15
4.16

5

4.4.1 Confidence intervals for 𝜇; 𝜎 known
4.4.2 Confidence intervals for 𝜇; 𝜎 unknown
4.4.3 Confidence intervals for 𝜎 2
4.4.4 Confidence intervals for p
Tolerance intervals
4.5.1 Tolerance intervals for the normal distributions
Testing for normality with probability plots
Tests of goodness of fit
4.7.1 The chi-square test (large samples)
4.7.2 The Kolmogorov-Smirnov test
Bayesian decision procedures
4.8.1 Prior and posterior distributions
4.8.2 Bayesian testing and estimation
4.8.3 Credibility intervals for real parameters
Random sampling from reference distributions
Bootstrap sampling
4.10.1 The bootstrap method
4.10.2 Examining the bootstrap method
4.10.3 Harnessing the bootstrap method
Bootstrap testing of hypotheses
4.11.1 Bootstrap testing and confidence intervals for the mean
4.11.2 Studentized test for the mean
4.11.3 Studentized test for the difference of two means
4.11.4 Bootstrap tests and confidence intervals for the variance
4.11.5 Comparing statistics of several samples
Bootstrap tolerance intervals
4.12.1 Bootstrap tolerance intervals for Bernoulli samples
4.12.2 Tolerance interval for continuous variables
4.12.3 Distribution-free tolerance intervals
Non-parametric tests
4.13.1 The sign test
4.13.2 The randomization test
4.13.3 The Wilcoxon Signed Rank test
Description of MINITAB macros (available for download from Appendix VI of the book website)
Chapter highlights
Exercises

129
130
130
130
132
132
134
137
137
139
140
141
144
147
148
150
150
151
152
152
153
153
155
157
158
161
161
163
164
165
165
166
168
170
170
171

Variability in Several Dimensions and Regression Models

177

5.1

177
177
179
181
182
185
185
185
187
192
194
198

5.2

5.3

5.4
5.5

Graphical display and analysis
5.1.1 Scatterplots
5.1.2 Multiple boxplots
Frequency distributions in several dimensions
5.2.1 Bivariate joint frequency distributions
5.2.2 Conditional distributions
Correlation and regression analysis
5.3.1 Covariances and correlations
5.3.2 Fitting simple regression lines to data
Multiple regression
5.4.1 Regression on two variables
Partial regression and correlation


x

Contents
5.6
5.7
5.8
5.9
5.10
5.11

5.12
5.13

5.14
5.15
5.16

Multiple linear regression
Partial F-tests and the sequential SS
Model construction: Step-wise regression
Regression diagnostics
Quantal response analysis: Logistic regression
The analysis of variance: The comparison of means
5.11.1 The statistical model
5.11.2 The one-way analysis of variance (ANOVA)
Simultaneous confidence intervals: Multiple comparisons
Contingency tables
5.13.1 The structure of contingency tables
5.13.2 Indices of association for contingency tables
Categorical data analysis
5.14.1 Comparison of binomial experiments
Chapter highlights
Exercises

200
204
206
209
211
213
213
214
216
220
220
223
227
227
229
230

PART II ACCEPTANCE SAMPLING

235

6

Sampling for Estimation of Finite Population Quantities

237

6.1

237
237
238
239
241
242
244
248
249
252
255
256

6.2

6.3
6.4
6.5
6.6
6.7

7

Sampling and the estimation problem
6.1.1 Basic definitions
6.1.2 Drawing a random sample from a finite population
6.1.3 Sample estimates of population quantities and their sampling distribution
Estimation with simple random samples
6.2.1 Properties of X n and Sn2 under RSWR
6.2.2 Properties of X n and Sn2 under RSWOR
Estimating the mean with stratified RSWOR
Proportional and optimal allocation
Prediction models with known covariates
Chapter highlights
Exercises

Sampling Plans for Product Inspection

258

7.1
7.2
7.3
7.4
7.5
7.6
7.7
7.8
7.9

258
259
262
264
267
270
272
274
276
276
278
279
280
281

7.10
7.11
7.12
7.13

General discussion
Single-stage sampling plans for attributes
Approximate determination of the sampling plan
Double-sampling plans for attributes
Sequential sampling
Acceptance sampling plans for variables
Rectifying inspection of lots
National and international standards
Skip-lot sampling plans for attributes
7.9.1 The ISO 2859 skip-lot sampling procedures
The Deming inspection criterion
Published tables for acceptance sampling
Chapter highlights
Exercises


Contents xi

PART III STATISTICAL PROCESS CONTROL

283

8

Basic Tools and Principles of Process Control

285

8.1
8.2
8.3
8.4
8.5
8.6
8.7

285
294
298
300
302
305
308
309
311
316
316

8.8
8.9

9

Advanced Methods of Statistical Process Control

319

9.1

319
319
321
323
324
325
328
328
328
330
330
333
335
339
342
346
347
348
350
351
354
356
357

9.2
9.3

9.4

9.5
9.6

9.7
9.8
9.9

10

Basic concepts of statistical process control
Driving a process with control charts
Setting up a control chart: Process capability studies
Process capability indices
Seven tools for process control and process improvement
Statistical analysis of Pareto charts
The Shewhart control charts
8.7.1 Control charts for attributes
8.7.2 Control charts for variables
Chapter highlights
Exercises

Tests of randomness
9.1.1 Testing the number of runs
9.1.2 Runs above and below a specified level
9.1.3 Runs up and down
9.1.4 Testing the length of runs up and down
Modified Shewhart control charts for X
The size and frequency of sampling for Shewhart control charts
9.3.1 The economic design for X-charts
9.3.2 Increasing the sensitivity of p-charts
Cumulative sum control charts
9.4.1 Upper Page’s scheme
9.4.2 Some theoretical background
9.4.3 Lower and two-sided Page’s scheme
9.4.4 Average run length, probability of false alarm and conditional expected delay
Bayesian detection
Process tracking
9.6.1 The EWMA procedure
9.6.2 The BECM procedure
9.6.3 The Kalman filter
9.6.4 Hoadley’s QMP
Automatic process control
Chapter highlights
Exercises

Multivariate Statistical Process Control

361

10.1
10.2
10.3
10.4

361
365
367
370
370
370
371
372

Introduction
A review of multivariate data analysis
Multivariate process capability indices
Advanced applications of multivariate control charts
10.4.1 Multivariate control charts scenarios
10.4.2 Internally derived targets
10.4.3 Using an external reference sample
10.4.4 Externally assigned targets


xii

Contents
10.4.5 Measurement units considered as batches
10.4.6 Variable decomposition and monitoring indices
10.5 Multivariate tolerance specifications
10.6 Chapter highlights
10.7 Exercises

373
373
374
376
377

PART IV DESIGN AND ANALYSIS OF EXPERIMENTS

379

11

12

Classical Design and Analysis of Experiments

381

11.1
11.2
11.3
11.4

Basic steps and guiding principles
Blocking and randomization
Additive and non-additive linear models
The analysis of randomized complete block designs
11.4.1 Several blocks, two treatments per block: Paired comparison
11.4.2 Several blocks, t treatments per block
11.5 Balanced incomplete block designs
11.6 Latin square design
11.7 Full factorial experiments
11.7.1 The structure of factorial experiments
11.7.2 The ANOVA for full factorial designs
11.7.3 Estimating main effects and interactions
11.7.4 2m factorial designs
11.7.5 3m factorial designs
11.8 Blocking and fractional replications of 2m factorial designs
11.9 Exploration of response surfaces
11.9.1 Second order designs
11.9.2 Some specific second order designs
11.9.3 Approaching the region of the optimal yield
11.9.4 Canonical representation
11.10 Chapter highlights
11.11 Exercises

381
385
385
387
387
391
394
397
402
402
402
408
409
417
425
430
431
433
438
440
441
442

Quality by Design

446

12.1 Off-line quality control, parameter design and the Taguchi method
12.1.1 Product and process optimization using loss functions
12.1.2 Major stages in product and process design
12.1.3 Design parameters and noise factors
12.1.4 Parameter design experiments
12.1.5 Performance statistics
12.2 The effects of non-linearity
12.3 Taguchi’s designs
12.4 Quality by design in the pharmaceutical industry
12.4.1 Introduction to quality by design
12.4.2 A quality by design case study – the full factorial design
12.4.3 A quality by design case study – the profiler and desirability function
12.4.4 A quality by design case study – the design space
12.5 Tolerance designs
12.6 More case studies
12.6.1 The Quinlan experiment at Flex Products, Inc.
12.6.2 Computer response time optimization

447
447
448
449
450
452
452
456
458
458
459
462
462
462
467
467
469


Contents xiii

13

12.7 Chapter highlights
12.8 Exercises

474
474

Computer Experiments

477

13.1
13.2
13.3
13.4
13.5
13.6
13.7

477
481
483
488
491
492
492

Introduction to computer experiments
Designing computer experiments
Analyzing computer experiments
Stochastic emulators
Integrating physical and computer experiments
Chapter highlights
Exercises

PART V RELIABILITY AND SURVIVAL ANALYSIS

495

14

15

Reliability Analysis

497

14.1 Basic notions
14.1.1 Time categories
14.1.2 Reliability and related functions
14.2 System reliability
14.3 Availability of repairable systems
14.4 Types of observations on TTF
14.5 Graphical analysis of life data
14.6 Non-parametric estimation of reliability
14.7 Estimation of life characteristics
14.7.1 Maximum likelihood estimators for exponential TTF distribution
14.7.2 Maximum likelihood estimation of the Weibull parameters
14.8 Reliability demonstration
14.8.1 Binomial testing
14.8.2 Exponential distributions
14.9 Accelerated life testing
14.9.1 The Arrhenius temperature model
14.9.2 Other models
14.10 Burn-in procedures
14.11 Chapter highlights
14.12 Exercises

498
498
499
500
503
509
510
513
514
514
518
520
520
521
528
528
529
529
530
531

Bayesian Reliability Estimation and Prediction

534

15.1 Prior and posterior distributions
15.2 Loss functions and Bayes estimators
15.2.1 Distribution-free Bayes estimator of reliability
15.2.2 Bayes estimator of reliability for exponential life distributions
15.3 Bayesian credibility and prediction intervals
15.3.1 Distribution-free reliability estimation
15.3.2 Exponential reliability estimation
15.3.3 Prediction intervals
15.4 Credibility intervals for the asymptotic availability of repairable systems: The exponential case
15.5 Empirical Bayes method
15.6 Chapter highlights
15.7 Exercises

534
537
538
538
539
539
540
540
542
543
545
545


xiv

Contents

List of R Packages
References and Further Reading

547
549

Author Index
Subject Index

555
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


Preface to Second Edition
This book is about modern industrial statistics and it applications using R, MINITAB and JMP. It is an expanded second
edition of a book entitled Modern Industrial Statistics: Design and Control of Quality and Reliability, Wadsworth Duxbury
Publishing, 1998, Spanish edition: Estadistica Industrial Moderna: Diseño y Control de Calidad y la Confiabilidad,
Thomson International, 2000. Abbreviated edition: Modern Statistics: A Computer-based Approach, Thomson Learning,
2001. Chinese edition: China Statistics Press, 2003 and Softcover edition, Brooks-Cole, 2004.
The pedagogical structure of the book combines a practical approach with theoretical foundations and computer support. It is intended for students and instructors who have an interest in studying modern methods by combining these
three elements. The first edition referred to S-Plus, MINITAB and compiled QuickBasic code. In this second edition we
provide examples and procedures in the now popular R language and also refer to MINITAB and JMP. Each of these
three computer platforms carries unique advantages. Focusing on only one or two of these is also possible. Exercises are
provided at the end of each chapter in order to provide more opportunities to learn and test your knowledge.
R is an open source programming language and software environment for statistical computing and graphics based
on the S programming language created by John Chambers while at Bell Labs in 1976. It is now developed by the R
Development Core Team, of which Chambers is a member. MINITAB is a statistics package originally developed at
the Pennsylvania State University by Barbara Ryan, Thomas Ryan, Jr., and Brian Joiner in 1972. MINITAB began as a
light version of OMNITAB, a statistical analysis program developed at the National Bureau of Standards now called the
National Institute of Standards and Technology (NIST). JMP was originally written in 1989 by John Sall and others to
perform simple and complex statistical analyses by dynamically linking statistics with graphics to interactively explore,
understand, and visualize data. JMP stands for John’s Macintosh Project and it is a division of SAS Institute Inc.
A clear advantage of R is that it is free open source software. It requires, however, knowledge of command language
programming. To help the reader, we developed a special comprehensive R application called mistat that includes all the
R programs used in the book. MINITAB is a popular statistical software application providing extensive collaboration and
reporting capabilities. JMP, a product of the SAS company, is also very popular and carries advanced scripting features and
high level visualization components. Both R and JMP have fully compatible versions for Mac OS. We do not aim to teach
programming in R or using MINITAB or JMP. We also do not cover all the options and features available in MINITAB
and JMP. Our aim is to expose students, researchers and practitioners of modern industrial statistics to examples of what
can be done with these software platforms and encourage the exploration of additional options in MINITAB and JMP.
Eventually, availability and convenience determine what software is used in specific cirmcumstances. We provide here
an opportunity to learn and get acquainted with three leading modern industrial statistics software platforms. A specially
prepared appendix, downloadable from the book website, provides an introduction to R. Also available for download
are the R scripts we refer to, organized by chapter. Installations of JMP and MINITAB include effective tutorials with
introductory material. Such tutorials have not been replicated in this text. To take full advantage of this book you need
to be interested in industrial statistics, have a proper mathematical background and be willing to learn by working out
problems with software applications. The five parts of the book can be studied in various combinations with Part I used
as a foundation. The book can therefore be used in workshops or courses on Acceptance Sampling, Statistical Process
Control, Design of Experiments and Reliability.
The three software platforms we refer to provide several simulation options. We believe that teaching modern industrial
statistics, with simulations, provides the right context for gaining sound hands-on experience. We aim at the middle road
target, between theoretical treatment and a cookbook approach. To achieve this, we provide over 40 data sets representing
real-life case studies which are typical of what one finds while performing statistical work in business and industry.
Figures in the book have been produced with R and in MINITAB and JMP as explicitly stated. In this second edition we
include contributions by Dr. Daniele Amberti who developed the R mistat package and provided many inputs to the
text. His work was supported by i4C (www.i4CAnalytics.com) and we are very grateful for it. Another change in this


xvi

Preface to Second Edition

second edition is that it is published by John Wiley and Sons and we would like to thank Heather Kay and Richard Davies
for their professional support and encouragements throughout this effort. We also acknowledge the contribution of Ian
Cox from JMP who developed for us the simulation code running the piston simulator and the bootstrapping analysis.
Thanks are due to the Genova Hotel in front of Porta Nuova in Turin where we spend countless hours updating the text
using push and pull of LaTeX files in Git from within RStudio. In fact, the whole book, with its R code and data sets has
been fully compiled in its present form to create an example of what reproducible research is all about. Gerry Hahn, Murat
Testik, Moshe Pollak, Gejza Dohnal, Neil Ullman, Moshe Miller, David Steinberg, Marcello Fidaleo, Inbal Yahav and Ian
Cox provided feedback on early drafts of the new chapters included in this second expanded edition of the original 1998
book–we thank them for their insightful comments. Finally, we would like to acknowledge the help of Marco Giuliano
who translated most of TeX files from the 1998 edition to LaTeX and of Marge Pratt who helped produce the final LaTeX
version of our work.
The book is accompanied by a dedicated website where all software and data files used are available to download.
The book website URL is www.wiley.com/go/modern_industrial_statistics.
The site contains: 1) all the R code included in the book which is also available on the R CRAN website as the mistat
package (folder scripts), 2) a source version of the mistat package for R (mistat_1.0.tar.gz), 3) all data sets as csv files
(csvFiles.zip and folder csvFiles in package mistat), 4) the MINITAB macros and JMP add-ins used in the text, 5)
an introduction to R prepared by Professor Stefano Iacus from the University of Milan and 6) solutions to some of the
exercises. Specifically, the book web site includes eight appendices: Appendix I - Introduction to R, by Stefano Iacus,
Appendix II - Basic MINITAB commands and a review of matrix algebra for Statistics, Appendix III - R code included
in the book, Appendix IV - Source version of package mistat, Appendix V - Data sets as csv files, Appendix VI MINITAB macros, Appendix VII - JMP scripts, by Ian Cox and Appendix VIII - Solution manual.
Special thanks are due to Professor Iacus for his generous contribution. If you are not familiar with R, we recommend
you look at this introduction specially prepared by one of the most important core developers of R. The material on the
book website should be considered part of the book. We obviously look forward to feedback, comments and suggestions
from students, teachers, researchers and practitioners and hope the book will help these different target groups achieve
concrete and significant impact with the tools and methods of industrial statistics.
Ron S. Kenett
Raanana, Israel and Turin, Italy
ron@kpa-group.com
Shelemyahu Zacks
Binghamton, New York, USA
shelly@math.binghamton.edu


Preface to First Edition
Modern Industrial Statistics provides the tools for those who drive to achieve perfection in industrial processes. Learn the
concepts and methods contained in this book and you will understand what it takes to measure and improve world-class
products and services.
The need for constant improvement of industrial processes, in order to achieve high quality, reliability, productivity
and profitability, is well recognized. Furthermore management techniques, such as total quality management or business
process reengineering, are insufficient in themselves to achieve the goal without the strong backing of specially tailored
statistical procedures, as stated by Robert Galvin in the Foreword.
Statistical procedures, designed for solving industrial problems, are called Industrial Statistics. Our objective in writing
this book was to provide statistics and engineering students, as well as practitioners, the concepts, applications, and
practice of basic and advanced industrial statistical methods, which are designed for the control and improvement of
quality and reliability.
The idea of writing a text on industrial statistics developed after several years of collaboration in industrial consulting, teaching workshops and seminars, and courses at our universities. We felt that no existing text served our needs in
both content and approach so we decided to develop our notes into a text. Our aim was to make the text modern and
comprehensive in terms of the techniques covered, lucid in its presentation, and practical with regard to implementation.
Ron S. Kenett
Shelemyahu Zacks
1998



Abbreviations
ANOVA
ANSI
AOQ
AOQL
AQL
ARL
ASN
ASQ
ATI
BECM
BIBD
BP
CAD
CADD
CAM
CBD
c.d.f.
CED
cGMP
CIM
CLT
CNC
CPA
CQA
CUSUM
DFIT
DLM
DoE
EBD
EWMA
FPM
GRR
HPD
i.i.d.
InfoQ
IQR
ISC
KS
LCL
LLN
LSE
LQL
LSL

Analysis of Variance
American National Standard Institute
Average Outgoing Quality
Average Outgoing Quality Limit
Acceptable Quality Level
Average Run Length
Average Sample Number
American Society for Quality
Average Total Inspection
Bayes Estimation of the Current Mean
Balanced Incomplete Block Design
Bootstrap Population
Computer Aided Design
Computer Aided Drawing and Drafting
Computer Aided Manufacturing
Complete Block Design
cumulative distribution function
Conditional Expected Delay
current good manufacturing practices
Computer Integrated Manufacturing
Central Limit Theorem
Computerized Numerically Controlled
Circuit Pack Assemblies
Critical Quality Attribute
Cumulative Sum
Fits distance
Dynamic Linear Model
Design of Experiments
Empirical Bootstrap Distribution
Exponentially Weighted Moving Average
Failures Per Million
Gage Repeatability and Reproducibility
Highest Posterior Density
independent and identically distributed
Information Quality
Inter Quartile Range
Short Circuit Current of Solar Cells (in Ampere)
Kolmogorov-Smirnov
Lower Control Limit
Law of Large Numbers
Least Squares Estimators
Limiting Quality Level
Lower Specification Limit


xx

Abbreviations

LTPD
LWL
m.g.f.
MLE
MSD
MTBF
MTTF
OC
p.d.f.
PERT
PFA
PL
PPM
PSE
QbD
QMP
QQ-Plot
RCBD
RMSE
RSWOR
RSWR
SE
SL
SLOC
SLSP
SPC
SPRT
SR
SSE
SSR
SST
STD
TTC
TTF
TTR
TTT
UCL
USL
UWL
WSP

Lot Tolerance Percent Defective
Lower Warning Limit
moment generating function
Maximum Likelihood Estimator
Mean Squared Deviation
Mean Time Between Failures
Mean Time To Failure
Operating Characteristic
probability density function
Project Evaluation and Review Technique
Probability of False Alarm
Product Limit Estimator
Defects in Parts Per Million
Practical Statistical Efficiency
Quality by Design
Quality Measurement Plan
Quantile vs. Quantile Plot
Randomized Complete Block Design
Root Mean Squared Error
Random Sample Without Replacement
Random Sample With Replacement
Standard Error
Skip Lot
Source Lines of Code
Skip Lot Sampling Plans
Statistical Process Control
Sequential Probability Ratio Test
Shiryayev-Roberts
Sum of Squares of Residuals
Sum of Squares Around the Regression Model
Total Sum of Squares
Standard Deviation
Time Till Censoring
Time Till Failure
Time Till Repair
Total Time on Test
Upper Control Limit
Upper Specification Limit
Upper Warning Limit
Wave Soldering Process


Part I
Principles of Statistical Thinking and Analysis
Part I is an introduction to the role of statistics in modern industry and service organizations, and to statistical thinking in
general. Typical industrial problems are described and basic statistical concepts and tools are presented through case studies and computer simulations. To help focus on data analysis and interpretation of results we refer, throughout the book, to
three leading software platforms for statistical analysis: R, MINITAB and JMP. R is an open source programming language
with more than 4300 application packages available at the Comprehensive R Archive Network (http://cran.r-project.org/).
MINITAB and JMP are statistical packages widely used in business and industry (www.minitab.com, www.jmp.com) with
30 days free fully functional downloads. The R code is integrated in the text and packaged in the mistat R application
that can be downloaded from CRAN or the book’s website www.wiley.com/go/modern_industrial_statistics. For ease of
use the mistat R applications are also organized by chapter in a set of scripts. The chapter scripts include all the R
examples used in the specific chapter.
Chapter 1 is an overview of the role of statistical methods in industry and offers a classification of statistical methods in
the context of various management approaches. We call this classification the Quality Ladder and use it to organize the
methods of industrial statistics covered in Parts II–V. The chapter also introduces the reader to the concept of practical
statistical efficiency (PSE) and information quality (InfoQ) that are used to assess the impact of work performed on a
given data set with statistical tools and the quality of knowledge generated by statistical analysis.
Chapter 2 presents basic concepts and tools for describing variability. It emphasizes graphical techniques to explore
and summarize variability in observations. The chapter introduces the reader to R and provides examples in MINITAB
and JMP. The examples demonstrate capabilities but it was not our intention to present introductory material on how to
use these software applications. Some help in this can be found in the book downloadable appendices.
Chapter 3 is an introduction to probability models including a comprehensive treatment of statistical distributions that
have applicability to industrial statistics. The chapter provides a reference to fundamental results and basic principles used
in later chapters.
Chapter 4 is dedicated to statistical inference and bootstrapping. Bootstrapping is introduced with examples in R,
MINITAB and JMP. With this approach, statistical procedures used in making inference from a sample to a population
are handled by computer-intensive procedures without the traditional need to validate mathematical assumptions and
models.
Chapter 5 deals with variability in several dimensions and regression models. It begins with graphical techniques that
handle observations taken simultaneously on several variables. These techniques are now widely available in software
applications such as those used throughout the book. The chapter covers linear and multiple regression models including
diagnostics and prediction intervals. Categorical data and multi-dimensional contingency tables are also analyzed.

Modern Industrial Statistics: with applications in R, MINITAB and JMP, Second Edition. Ron S. Kenett and Shelemyahu Zacks.
© 2014 John Wiley & Sons, Ltd. Published 2014 by John Wiley & Sons, Ltd.
Companion website: www.wiley.com/go/modern_industrial_statistics



1
The Role of Statistical Methods in
Modern Industry and Services
1.1

The different functional areas in industry and services

Industrial statistics has played a key role in the creation of competitiveness in a wide range of organizations in the industrial sector, in services, health care, government and educational systems. The tools and concepts of industrial statistics
have to be viewed in the context of their applications. These applications are greatly affected by management style and
organizational culture. We begin by describing key aspects of the industrial setting in order to lay out the foundations for
the book.
Industrial organizations typically include units dedicated to product development, manufacturing, marketing, finance,
human resources, purchasing, sales, quality assurance and after-sales support. Industrial statistics is used to resolve problems in each one of these functional units. Marketing personnel determine customer requirements and measure levels
of customer satisfaction using surveys and focus groups. Sales are responsible for providing forecasts to purchasing and
manufacturing. Purchasing specialists analyze world trends in quality and prices of raw materials so that they can optimize
costs and delivery time. Budgets are prepared by the finance department using forecasts that are validated periodically.
Accounting experts rely on auditing and sampling methods to ascertain inventory levels and integrity of databases. Human
resources personnel track data on absenteeism, turnover, overtime and training needs. They also conduct employee surveys and deploy performance appraisal systems. The quality departments commonly perform audits and quality tests,
to determine and ensure the quality and reliability of products and services. Research and development engineers perform experiments to solve problems and improve products and processes. Finally, manufacturing personnel and process
engineers design process controls for production operations using control charts and automation.
These are only a few examples of problem areas where the tools of industrial statistics are used within modern industrial
and service organizations. In order to provide more specific examples we first take a closer look at a variety of industries.
Later we discuss examples from these types of industries.
There are basically three types of production systems: (1) continuous flow production; (2) job shops; and (3) discrete
mass production. Examples of continuous flow production include steel, glass and paper making, thermal power
generation and chemical transformations. Such processes typically involve expensive equipment that is very large
in size, operates around the clock and requires very rigid manufacturing steps. Continuous flow industries are both
capital-intensive and highly dependent on the quality of the purchased raw materials. Rapid customizing of products in a
continuous flow process is virtually impossible and new products are introduced using complex scale-up procedures.

Modern Industrial Statistics: with applications in R, MINITAB and JMP, Second Edition. Ron S. Kenett and Shelemyahu Zacks.
© 2014 John Wiley & Sons, Ltd. Published 2014 by John Wiley & Sons, Ltd.
Companion website: www.wiley.com/go/modern_industrial_statistics


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