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E-grāmata: Modern Industrial Statistics - With Applications in R, MINITAB and JMP, 3e: With Applications in R, MINITAB, and JMP 3rd Edition [Wiley Online]

(KPA Ltd., Israel), (Binghamton University)
  • Formāts: 880 pages
  • Sērija : Statistics in Practice
  • Izdošanas datums: 03-Jun-2021
  • Izdevniecība: John Wiley & Sons Inc
  • ISBN-10: 111971494X
  • ISBN-13: 9781119714941
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  • Wiley Online
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Modern Industrial Statistics - With Applications in R, MINITAB and JMP, 3e: With Applications in R, MINITAB, and JMP 3rd EditionPalielināt
  • Formāts: 880 pages
  • Sērija : Statistics in Practice
  • Izdošanas datums: 03-Jun-2021
  • Izdevniecība: John Wiley & Sons Inc
  • ISBN-10: 111971494X
  • ISBN-13: 9781119714941
Citas grāmatas par šo tēmu:
Wiley Online E-booksMore info about Wiley Online
Rokasgrāmatas mājas lapa: https://onlinelibrary.wiley.com/doi/book/10.1002/9781119714941

The new edition of the prime reference on the tools of statistics used in industry and services, integrating theoretical, practical, and computer-based approaches  

Modern Industrial Statistics is a leading reference and guide to the statistics tools widely used in industry and services. Designed to help professionals and students easily access relevant theoretical and practical information in a single volume, this standard resource employs a computer-intensive approach to industrial statistics and provides numerous examples and procedures in the popular R language and for MINITAB and JMP statistical analysis software. Divided into two parts, the text covers the principles of statistical thinking and analysis, bootstrapping, predictive analytics, Bayesian inference, time series analysis, acceptance sampling, statistical process control, design and analysis of experiments, simulation and computer experiments, and reliability and survival analysis. Part A, on computer age statistical analysis, can be used in general courses on analytics and statistics. Part B is focused on industrial statistics applications.  

The fully revised third edition covers the latest techniques in R, MINITAB and JMP, and features brand-new coverage of time series analysis, predictive analytics and Bayesian inference. New and expanded simulation activities, examples, and case studies—drawn from the electronics, metal work, pharmaceutical, and financial industries—are complimented by additional computer and modeling methods. Helping readers develop skills for modeling data and designing experiments, this comprehensive volume:  

  • Explains the use of computer-based methods such as bootstrapping and data visualization 
  • Covers nonstandard techniques and applications of industrial statistical process control (SPC) charts 
  • Contains numerous problems, exercises, and data sets representing real-life case studies of statistical work in various business and industry settings 
  • Includes access to a companion website that contains an introduction to R, sample R code, csv files of all data sets, JMP add-ins, a solutions manual, and downloadable appendices 
  • Provides an author-created R package, mistat, that includes all data sets and statistical analysis applications used in the book  

Part of the acclaimed Statistics in Practice series, Modern Industrial Statistics with Applications in R, MINITAB, and JMP, Third Edition, is the perfect textbook for advanced undergraduate and postgraduate courses in the areas of industrial statistics, quality and reliability engineering, and an important reference for industrial statisticians, researchers, and practitioners in related fields.  

Preface to Third Edition xvii
Preface to Second Edition xix
Preface to First Edition xxi
List of Abbreviations
xxiii
PART I MODERN STATISTICS: A COMPUTER-BASED APPROACH
1(346)
1 Statistics and Analytics in Modern Industry
3(10)
1.1 Analytics, big data, and the fourth industrial revolution
3(1)
1.2 Computer age analytics
4(2)
1.3 The analytics maturity ladder
6(2)
1.4 Information quality
8(2)
1.5
Chapter highlights
10(1)
1.6 Exercises
10(3)
2 Analyzing Variability: Descriptive Statistics
13(34)
2.1 Random phenomena and the structure of observations
13(5)
2.2 Accuracy and precision of measurements
18(2)
2.3 The population and the sample
20(1)
2.4 Descriptive analysis of sample values
20(14)
2.4.1 Frequency distributions of discrete random variables
20(5)
2.4.2 Frequency distributions of continuous random variables
25(3)
2.4.3 Statistics of the ordered sample
28(2)
2.4.4 Statistics of location and dispersion
30(4)
2.5 Prediction intervals
34(2)
2.6 Additional techniques of exploratory data analysis
36(6)
2.6.1 Box and whiskers plot
36(1)
2.6.2 Quantile plots
37(1)
2.6.3 Stem-and-leaf diagrams
38(1)
2.6.4 Robust statistics for location and dispersion
38(4)
2.7
Chapter highlights
42(1)
2.8 Exercises
42(5)
3 Probability Models and Distribution Functions
47(84)
3.1 Basic probability
47(12)
3.1.1 Events and sample spaces: formal presentation of random measurements
47(2)
3.1.2 Basic rules of operations with events: unions, intersections
49(2)
3.1.3 Probabilities of events
51(2)
3.1.4 Probability functions for random sampling
53(2)
3.1.5 Conditional probabilities and independence of events
55(2)
3.1.6 Bayes' formula and its application
57(2)
3.2 Random variables and their distributions
59(11)
3.2.1 Discrete and continuous distributions
60(1)
3.2.1.1 Discrete random variables
60(2)
3.2.1.2 Continuous random variables
62(2)
3.2.2 Expected values and moments of distributions
64(2)
3.2.3 The standard deviation, quantiles, measures of skewness and kurtosis
66(3)
3.2.4 Moment generating functions
69(1)
3.3 Families of discrete distribution
70(10)
3.3.1 The binomial distribution
70(3)
3.3.2 The hypergeometric distribution
73(3)
3.3.3 The poisson distribution
76(2)
3.3.4 The geometric and negative binomial distributions
78(2)
3.4 Continuous distributions
80(14)
3.4.1 The uniform distribution on the interval (a, b), a > b
80(1)
3.4.2 The normal and log-normal distributions
81(1)
3.4.2.1 The normal distribution
81(5)
3.4.2.2 The log-normal distribution
86(2)
3.4.3 The exponential distribution
88(1)
3.4.4 The Gamma and Weibull distributions
89(3)
3.4.5 The Beta distributions
92(2)
3.5 Joint, marginal and conditional distributions
94(8)
3.5.1 Joint and marginal distributions
94(3)
3.5.2 Covariance and correlation
97(1)
3.5.2.1 Definition of independence
98(1)
3.5.3 Conditional distributions
99(3)
3.6 Some multivariate distributions
102(5)
3.6.1 The multinomial distribution
102(1)
3.6.2 The multi-hypergeometric distribution
103(2)
3.6.3 The bivariate normal distribution
105(2)
3.7 Distribution of order statistics
107(2)
3.8 Linear combinations of random variables
109(5)
3.9 Large sample approximations
114(3)
3.9.1 The law of large numbers
114(1)
3.9.2 The central limit theorem
114(1)
3.9.3 Some normal approximations
115(2)
3.10 Additional distributions of statistics of normal samples
117(4)
3.10.1 Distribution of the sample variance
117(1)
3.10.2 The "Student" f-statistic
118(1)
3.10.3 Distribution of the variance ratio
119(2)
3.11
Chapter highlights
121(1)
3.12 Exercises
122(1)
Additional problems in combinatorial and geometric probabilities
123(8)
4 Statistical Inference and Bootstrapping
131(74)
4.1 Sampling characteristics of estimators
131(2)
4.2 Some methods of point estimation
133(6)
4.2.1 Moment equation estimators
134(1)
4.2.2 The method of least squares
135(2)
4.2.3 Maximum likelihood estimators
137(2)
4.3 Comparison of sample estimates
139(10)
4.3.1 Basic concepts
139(3)
4.3.2 Some common one-sample tests of hypotheses
142(1)
4.3.2.1 The Z-test: testing the mean of a normal distribution, er2 known
142(2)
4.3.2.2 The f-test: testing the mean of a normal distribution, a2 unknown
144(1)
4.3.2.3 The chi-squared test: testing the variance of a normal distribution
145(2)
4.3.2.4 Testing hypotheses about the success probability, p, in binomial trials
147(2)
4.4 Confidence intervals
149(4)
4.4.1 Confidence intervals for μ σ known
150(1)
4.4.2 Confidence intervals for μ σ unknown
150(1)
4.4.3 Confidence intervals for σ2
151(1)
4.4.4 Confidence intervals for p
151(2)
4.5 Tolerance intervals
153(3)
4.5.1 Tolerance intervals for the normal distributions
153(3)
4.6 Testing for normality with probability plots
156(3)
4.7 Tests of goodness of fit
159(3)
4.7.1 The chi-square test (large samples)
159(3)
4.7.2 The Kolmogorov-Smirnov test
162(1)
4.8 Bayesian decision procedures
162(10)
4.8.1 Prior and posterior distributions
163(4)
4.8.2 Bayesian testing and estimation
167(1)
4.8.2.1 Bayesian testing
167(3)
4.8.2.2 Bayesian estimation
170(1)
4.8.3 Credibility intervals for real parameters
170(2)
4.9 Random sampling from reference distributions
172(2)
4.10 Bootstrap sampling
174(2)
4.10.1 The bootstrap method
174(1)
4.10.2 Examining the bootstrap method
175(1)
4.10.3 Harnessing the bootstrap method
176(1)
4.11 Bootstrap testing of hypotheses
176(10)
4.11.1 Bootstrap testing and confidence intervals for the mean
177(1)
4.11.2 Studentized test for the mean
177(2)
4.11.3 Studentized test for the difference of two means
179(3)
4.11.4 Bootstrap tests and confidence intervals for the variance
182(1)
4.11.5 Comparing statistics of several samples
183(1)
4.11.5.1 Comparing variances of several samples
183(1)
4.11.5.2 Comparing several means: the one-way analysis of variance
184(2)
4.12 Bootstrap tolerance intervals
186(5)
4.12.1 Bootstrap tolerance intervals for Bernoulli samples
186(2)
4.12.2 Tolerance interval for continuous variables
188(2)
4.12.3 Distribution free tolerance intervals
190(1)
4.13 Nonparametric tests
191(6)
4.13.1 The sign test
191(2)
4.13.2 The randomization test
193(2)
4.13.3 The Wilcoxon signed rank test
195(2)
4.14 Description of MINITAB macros
197(1)
4.15
Chapter highlights
197(1)
4.16 Exercises
198(7)
5 Variability in Several Dimensions and Regression Models
205(68)
5.1 Graphical display and analysis
205(5)
5.1.1 Scatterplots
205(3)
5.1.2 Multiple box-plots
208(2)
5.2 Frequency distributions in several dimensions
210(5)
5.2.1 Bivariate joint frequency distributions
211(3)
5.2.2 Conditional distributions
214(1)
5.3 Correlation and regression analysis
215(8)
5.3.1 Covariances and correlations
215(3)
5.3.2 Fitting simple regression lines to data
218(1)
5.3.2.1 The least squares method
218(5)
5.3.2.2 Regression and prediction intervals
223(1)
5.4 Multiple regression
223(6)
5.4.1 Regression on two variables
225(4)
5.5 Partial regression and correlation
229(3)
5.6 Multiple linear regression
232(5)
5.7 Partial F-tests and the sequential SS
237(2)
5.8 Model construction: stepwise regression
239(3)
5.9 Regression diagnostics
242(3)
5.10 Quantal response analysis: logistic regression
245(2)
5.11 The analysis of variance: the comparison of means
247(4)
5.11.1 The statistical model
247(1)
5.11.2 The one-way analysis of variance (ANOVA)
247(4)
5.12 Simultaneous confidence intervals: multiple comparisons
251(3)
5.13 Contingency tables
254(9)
5.13.1 The structure of contingency tables
254(4)
5.13.2 Indices of association for contingency tables
258(1)
5.13.2.1 Two interval scaled variables
258(1)
5.13.2.2 Indices of association for categorical variables
259(4)
5.14 Categorical data analysis
263(2)
5.14.1 Comparison of binomial experiments
263(2)
5.15
Chapter highlights
265(1)
5.16 Exercises
266(7)
6 Sampling for Estimation of Finite Population Quantities
273(24)
6.1 Sampling and the estimation problem
273(5)
6.1.1 Basic definitions
273(1)
6.1.2 Drawing a random sample from a finite population
274(1)
6.1.3 Sample estimates of population quantities and their sampling distribution
275(3)
6.2 Estimation with simple random samples
278(7)
6.2.1 Properties of Xn and S2 under RSWR
279(3)
6.2.2 Properties of Xn and S2n under RSWOR
282(3)
6.3 Estimating the mean with stratified RSWOR
285(2)
6.4 Proportional and optimal allocation
287(3)
6.5 Prediction models with known covariates
290(5)
6.6
Chapter highlights
295(1)
6.7 Exercises
295(2)
7 Time Series Analysis and Prediction
297(28)
7.1 The components of a time series
297(4)
7.1.1 The trend and covariances
297(1)
7.1.2 Applications with MINITAB and JMP
298(3)
7.2 Covariance stationary time series
301(7)
7.2.1 Moving averages
302(1)
7.2.2 Auto-regressive time series
302(3)
7.2.3 Auto-regressive moving averages time series
305(1)
7.2.4 Integrated auto-regressive moving average time series
306(1)
7.2.5 Applications with JMP and R
307(1)
7.3 Linear predictors for covariance stationary time series
308(1)
7.3.1 Optimal linear predictors
308(1)
7.4 Predictors for nonstationary time series
309(3)
7.4.1 Quadratic LSE predictors
309(2)
7.4.2 Moving average smoothing predictors
311(1)
7.5 Dynamic linear models
312(5)
7.5.1 Some special cases
313(4)
7.6
Chapter highlights
317(1)
7.7 Exercises
318(7)
Appendix
320(5)
8 Modern Analytic Methods
325(22)
8.1 Introduction to computer age statistics
325(1)
8.2 Decision trees
326(5)
8.3 Naive Bayes classifier
331(3)
8.4 Clustering methods
334(3)
8.5 Functional data analysis
337(3)
8.6 Text analytics
340(3)
8.7
Chapter highlights
343(1)
8.8 Exercises
344(3)
PART II MODERN INDUSTRIAL STATISTICS: DESIGN AND CONTROL OF QUALITY AND RELIABILITY
347(354)
9 The Role of Statistical Methods in Modern Industry and Services
349(12)
9.1 The different functional areas in industry and services
349(2)
9.2 The quality-productivity dilemma
351(2)
9.3 Firefighting
353(1)
9.4 Inspection of products
354(1)
9.5 Process control
355(1)
9.6 Quality by design
355(3)
9.7 Practical statistical efficiency
358(1)
9.8
Chapter highlights
359(1)
9.9 Exercises
360(1)
10 Basic Tools and Principles of Process Control
361(40)
10.1 Basic concepts of statistical process control
361(10)
10.2 Driving a process with control charts
371(4)
10.3 Setting up a control chart: process capability studies
375(2)
10.4 Process capability indices
377(3)
10.5 Seven tools for process control and process improvement
380(4)
10.5.1 Flowcharts
380(1)
10.5.2 Check sheets
380(1)
10.5.3 Run charts
380(1)
10.5.4 Histograms
381(1)
10.5.5 Pareto charts
381(1)
10.5.6 Scatterplots
382(1)
10.5.7 Cause and effect diagrams
382(2)
10.6 Statistical analysis of Pareto charts
384(3)
10.7 The Shewhart control charts
387(8)
10.7.1 Control charts for attributes
388(2)
10.7.2 Control charts for variables
390(5)
10.8
Chapter highlights
395(1)
10.9 Exercises
396(5)
11 Advanced Methods of Statistical Process Control
401(48)
11.1 Tests of randomness
401(7)
11.1.1 Testing the number of runs
402(1)
11.1.2 Runs above and below a specified level
403(3)
11.1.3 Runs up and down
406(1)
11.1.4 Testing the length of runs up and down
407(1)
11.2 Modified Shewhart control charts for X
408(3)
11.3 The size and frequency of sampling for Shewhart control charts
411(3)
11.3.1 The economic design for X-charts
411(1)
11.3.2 Increasing the sensitivity of p-charts
411(3)
11.4 Cumulative sum control charts
414(13)
11.4.1 Upper Page's scheme
414(3)
11.4.2 Some theoretical background
417(1)
11.4.2.1 Normal distribution
418(1)
11.4.2.2 Binomial distributions
418(1)
11.4.2.3 Poisson distributions
419(1)
11.4.3 Lower and two-sided Page's scheme
419(4)
11.4.4 Average run length, probability of false alarm, and conditional expected delay
423(4)
11.5 Bayesian detection
427(5)
11.6 Process tracking
432(9)
11.6.1 The EWMA procedure
433(2)
11.6.2 The BECM procedure
435(1)
11.6.3 The Kalman filter
436(3)
11.6.4 Hoadley's QMP
439(2)
11.7 Automatic process control
441(3)
11.8
Chapter highlights
444(1)
11.9 Exercises
444(5)
12 Multivariate Statistical Process Control
449(22)
12.1 Introduction
449(5)
12.2 A review multivariate data analysis
454(3)
12.3 Multivariate process capability indices
457(3)
12.4 Advanced applications of multivariate control charts
460(4)
12.4.1 Multivariate control charts scenarios
460(1)
12.4.2 Internally derived targets
460(1)
12.4.3 Using an external reference sample
461(1)
12.4.4 Externally assigned targets
462(1)
12.4.5 Measurement units considered as batches
463(1)
12.4.6 Variable decomposition and monitoring indices
464(1)
12.5 Multivariate tolerance specifications
464(3)
12.6
Chapter highlights
467(1)
12.7 Exercises
468(3)
13 Classical Design and Analysis of Experiments
471(74)
13.1 Basic steps and guiding principles
471(4)
13.2 Blocking and randomization
475(1)
13.3 Additive and nonadditive linear models
476(2)
13.4 The analysis of randomized complete block designs
478(8)
13.4.1 Several blocks, two treatments per block: paired comparison
478(1)
13.4.1.1 The t-test
479(1)
13.4.1.2 Randomization tests
479(4)
13.4.2 Several blocks, t treatments per block
483(3)
13.5 Balanced incomplete block designs
486(4)
13.6 Latin square design
490(5)
13.7 Full factorial experiments
495(26)
13.7.1 The structure of factorial experiments
495(1)
13.7.2 The ANOVA for full factorial designs
496(5)
13.7.3 Estimating main effects and interactions
501(2)
13.7.4 2m factorial designs
503(9)
13.7.5 3m factorial designs
512(9)
13.8 Blocking and fractional replications of 2m factorial designs
521(6)
13.9 Exploration of response surfaces
527(13)
13.9.1 Second-order designs
527(3)
13.9.2 Some specific second-order designs
530(7)
13.9.3 Approaching the region of the optimal yield
537(1)
13.9.4 Canonical representation
538(2)
13.10
Chapter highlights
540(1)
13.11 Exercises
541(4)
14 Quality by Design
545(36)
14.1 Off-line quality control, parameter design, and the Taguchi method
546(7)
14.1.1 Product and process optimization using loss functions
546(2)
14.1.2 Major stages in product and process design
548(1)
14.1.3 Design parameters and noise factors
548(2)
14.1.4 Parameter design experiments
550(2)
14.1.5 Performance statistics
552(1)
14.2 The effects of non-linearity
553(4)
14.3 Taguchi's designs
557(2)
14.4 Quality by design in the pharmaceutical industry
559(7)
14.4.1 Introduction to quality by design
559(1)
14.4.2 A quality by design case study - the full factorial design
560(4)
14.4.3 A quality by design case study - the profiler and desirability function
564(1)
14.4.4 A quality by design case study - the design space
564(2)
14.5 Tolerance designs
566(4)
14.6 Case studies
570(6)
14.6.1 The Quinlan experiment
570(2)
14.6.2 Computer response time optimization
572(4)
14.7
Chapter highlights
576(1)
14.8 Exercises
577(4)
15 Computer Experiments
581(22)
15.1 Introduction to computer experiments
581(4)
15.2 Designing computer experiments
585(6)
15.3 Analyzing computer experiments
591(4)
15.4 Stochastic emulators
595(1)
15.5 Integrating physical and computer experiments
596(2)
15.6 Simulation of random variables
598(2)
15.6.1 Basic procedures
598(1)
15.6.2 Generating random vectors
599(1)
15.6.3 Approximating integrals
600(1)
15.7
Chapter highlights
600(1)
15.8 Exercises
601(2)
16 Reliability Analysis
603(46)
16.1 Basic notions
605(2)
16.1.1 Time categories
605(1)
16.1.2 Reliability and related functions
606(1)
16.2 System reliability
607(3)
16.3 Availability of repairable systems
610(7)
16.4 Types of observations on TTF
617(1)
16.5 Graphical analysis of life data
618(4)
16.6 Nonparametric estimation of reliability
622(2)
16.7 Estimation of life characteristics
624(7)
16.7.1 Maximum likelihood estimators for exponential TTF distribution
625(4)
16.7.2 Maximum likelihood estimation of the Weibull parameters
629(2)
16.8 Reliability demonstration
631(9)
16.8.1 Binomial testing
631(1)
16.8.2 Exponential distributions
632(2)
16.8.2.1 The SPRT for binomial data
634(2)
16.8.2.2 The SPRT for exponential lifetimes
636(3)
16.8.2.3 The SPRT for Poisson processes
639(1)
16.9 Accelerated life testing
640(1)
16.9.1 The Arrhenius temperature model
640(1)
16.9.2 Other models
641(1)
16.10 Burn-in procedures
641(2)
16.11
Chapter highlights
643(1)
16.12 Exercises
643(6)
17 Bayesian Reliability Estimation and Prediction
649(22)
17.1 Prior and posterior distributions
649(4)
17.2 Loss functions and bayes estimators
653(2)
17.2.1 Distribution-free bayes estimator of reliability
654(1)
17.2.2 Bayes estimator of reliability for exponential life distributions
654(1)
17.3 Bayesian credibility and prediction intervals
655(7)
17.3.1 Distribution-free reliability estimation
656(1)
17.3.2 Exponential reliability estimation
656(1)
17.3.3 Prediction intervals
657(1)
17.3.4 Applications with JMP
658(4)
17.4 Credibility intervals for the asymptotic availability of repairable systems: the exponential case
662(3)
17.5 Empirical bayes method
665(2)
17.6
Chapter highlights
667(1)
17.7 Exercises
668(3)
18 Sampling Plans for Batch and Sequential Inspection
671(30)
18.1 General discussion
671(2)
18.2 Single-stage sampling plans for attributes
673(3)
18.3 Approximate determination of the sampling plan
676(2)
18.4 Double-sampling plans for attributes
678(3)
18.5 Sequential A/B testing
681(4)
18.5.1 The one-armed Bernoulli bandits
681(3)
18.5.2 Two-armed Bernoulli bandits
684(1)
18.6 Acceptance sampling plans for variables
685(2)
18.7 Rectifying inspection of lots
687(2)
18.8 National and international standards
689(1)
18.9 Skip-lot sampling plans for attributes
690(3)
18.9.1 The ISO 2859 skip-lot sampling procedures
691(2)
18.10 The Deming inspection criterion
693(1)
18.11 Published tables for acceptance sampling
694(1)
18.12
Chapter highlights
695(1)
18.13 Exercises
696(5)
Appendix
698(3)
List of R Packages 701(4)
Solution Manual 705(118)
References 823(6)
Author Index 829(4)
Subject Index 833
Ron S. Kenett is Chairman of the KPA Group and Senior Research Fellow at the Samuel Neaman Institute, Israel. He is an applied statistician combining expertise in academic, consulting, and business domains. He is a former Professor of Operations Management at The State University of New York at Binghamton, Visiting Scholar at Stanford University, Member of Technical Staff at Bell Laboratories and Director of Statistical Methods for Tadiran Telecom. Ron is a past President of the Israel Statistical Association and of the European Network for Business and Industrial Statistics (ENBIS) and was awarded the 2013 Greenfield Medal by the Royal Statistical Society and the 2018 Box Medal by ENBIS for outstanding contributions to applied statistics. He has authored and co-authored over 250 papers and 14 books.

Shelemyahu Zacks is Distinguished Emeritus Professor of Mathematical Sciences at Binghamton University, Binghamton, New York, USA. He has published 10 books and close to 200 papers. Zacks is known for his groundbreaking articles on change-point problems, common mean problems, Bayes sequential strategies, and reliability analysis. His studies on survival probabilities in crossing minefields and his contributions in stochastic visibility in random fields are regarded as fundamental work in naval research and other defense related areas. He has served on the editorial boards of several prestigious journals including JASA, JSPI and Annals of Statistics, and is a Fellow of many associations including the AMS, ASA and AAAS.
Permanent link: https://www.kriso.lv/db/9781119714941_pe.html
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