Kenett R., Zacks S., Amberti D. Modern Industrial Statistics: with applications in R, MINITAB and JMP

2nd ed. — Wiley, 2014. — 592 p. — (Statistics in Practice). — ISBN: 1118456068, 9781118456064.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.Preface to Second Edition
Preface to First Edition
AbbreviationsPrinciples of statistical thinking and analysis
The Role of Statistical Methods in Modern Industry and Services
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
ExercisesAnalyzing Variability: Descriptive Statistics
Random phenomena and the structure of observations
Accuracy and precision of measurements
The population and the sample
Descriptive analysis of sample values
Frequency distributions of discrete random variables
Frequency distributions of continuous random variables
Statistics of the ordered sample
Statistics of location and dispersion
Prediction intervals
Additional techniques of exploratory data analysis
Box and whiskers plot
Quantile plots
Stem-and-leaf diagrams
Robust statistics for location and dispersion
Chapter highlights
ExercisesProbability Models and Distribution Functions
Basic probability
Events and sample spaces: Formal presentation of random measurements
Basic rules of operations with events: Unions, intersections
Probabilities of events
viii Contents
Probability functions for random sampling
Conditional probabilities and independence of events
Bayes formula and its application
Random variables and their distributions
Discrete and continuous distributions
Expected values and moments of distributions
The standard deviation, quantiles, measures of skewness and kurtosis
Moment generating functions
Families of discrete distribution
The binomial distribution
The hypergeometric distribution
The Poisson distribution
The geometric and negative binomial distributions
Continuous distributions
The uniform distribution on the interval (a, b), a < b
The normal and log-normal distributions
The exponential distribution
The gamma and Weibull distributions
The Beta distributions
Joint, marginal and conditional distributions
Joint and marginal distributions
Covariance and correlation
Conditional distributions
Some multivariate distributions
The multinomial distribution
The multi-hypergeometric distribution
The bivariate normal distribution
Distribution of order statistics
Linear combinations of random variables
Large sample approximations
The law of large numbers
The Central Limit Theorem
Some normal approximations
Additional distributions of statistics of normal samples
Distribution of the sample variance
The “Student” t-statistic
Distribution of the variance ratio
Chapter highlights
ExercisesStatistical Inference and Bootstrapping
Sampling characteristics of estimators
Some methods of point estimation
Moment equation estimators
The method of least squares
Maximum likelihood estimators
Comparison of sample estimates
Basic concepts
Some common one-sample tests of hypotheses
Confidence intervals
Confidence intervals for ?; ? known
Confidence intervals for ?; ? unknown
Confidence intervals for?
Confidence intervals for p
Tolerance intervals
Tolerance intervals for the normal distributions
Testing for normality with probability plots
Tests of goodness of fit
The chi-square test (large samples)
The Kolmogorov-Smirnov test
Bayesian decision procedures
Prior and posterior distributions
Bayesian testing and estimation
Credibility intervals for real parameters
Random sampling from reference distributions
Bootstrap sampling
The bootstrap method
Examining the bootstrap method
Harnessing the bootstrap method
Bootstrap testing of hypotheses
Bootstrap testing and confidence intervals for the mean
Studentized test for the mean
Studentized test for the difference of two means
Bootstrap tests and confidence intervals for the variance
Comparing statistics of several samples
Bootstrap tolerance intervals
Bootstrap tolerance intervals for Bernoulli samples
Tolerance interval for continuous variables
Distribution-free tolerance intervals
Non-parametric tests
The sign test
The randomization test
The Wilcoxon Signed Rank test
Description of Minitab macros (available for download from Appendix Vi of the book website)
Chapter highlights
ExercisesVariability in Several Dimensions and Regression Models
Graphical display and analysis
Scatterplots
Multiple boxplots
Frequency distributions in several dimensions
Bivariate joint frequency distributions
Conditional distributions
Correlation and regression analysis
Covariances and correlations
Fitting simple regression lines to data
Multiple regression
Regression on two variables
Partial regression and correlation
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
The statistical model
The one-way analysis of variance (Anova)
simultaneous confidence intervals: Multiple comparisons
Contingency tables
The structure of contingency tables
Indices of association for contingency tables
Categorical data analysis
Comparison of binomial experiments
Chapter highlights
ExercisesAcceptance sampling
Sampling for Estimation of Finite Population Quantities
Sampling and the estimation problem
Basic definitions
Drawing a random sample from a finite population
Sample estimates of population quantities and their sampling distribution
Estimation with simple random samples
Properties of Xn and S n under Rswr
properties of Xn and S n under Rswor
estimating the mean with stratified Rswor
proportional and optimal allocation
Prediction models with known covariates
Chapter highlights
ExercisesSampling Plans for Product Inspection
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
The Iso skip-lot sampling procedures
The Deming inspection criterion
Published tables for acceptance sampling
Chapter highlights
ExercisesStatistical process control
Basic Tools and Principles of Process Control
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
Control charts for attributes
Control charts for variables
Chapter highlights
ExercisesAdvanced Methods of Statistical Process Control
Tests of randomness
Testing the number of runs
Runs above and below a specified level
Runs up and down
Testing the length of runs up and down
Modified Shewhart control charts for X
the size and frequency of sampling for Shewhart control charts
The economic design for X-charts
Increasing the sensitivity of p-charts
Cumulative sum control charts
Upper Page’s scheme
Some theoretical background
Lower and two-sided Page’s scheme
Average run length, probability of false alarm and conditional expected delay
Bayesian detection
Process tracking
The Ewma procedure
The Becm procedure
The Kalman filter
Hoadley’s Qmp
automatic process control
Chapter highlights
ExercisesMultivariate Statistical Process ControlA review of multivariate data analysis
Multivariate process capability indices
Advanced applications of multivariate control charts
Multivariate control charts scenarios
Internally derived targets
Using an external reference sample
Externally assigned targets
Measurement units considered as batches
Variable decomposition and monitoring indices
Multivariate tolerance specifications
Chapter highlights
ExercisesDesign and analysis of experiments
Classical Design and Analysis of Experiments
Basic steps and guiding principles
Blocking and randomization
Additive and non-additive linear models
The analysis of randomized complete block designs
Several blocks, two treatments per block: Paired comparison
Several blocks, t treatments per block
Balanced incomplete block designs
Latin square design
Full factorial experiments
The structure of factorial experiments
The Anova for full factorial designs
Estimating main effects and interactions
m factorial designs
m factorial designs
Blocking and fractional replications of m factorial designs
Exploration of response surfaces
Second order designs
Some specific second order designs
Approaching the region of the optimal yield
Canonical representation
Chapter highlights
ExercisesQuality by Design
Off-line quality control, parameter design and the Taguchi method
Product and process optimization using loss functions
Major stages in product and process design
Design parameters and noise factors
Parameter design experiments
Performance statistics
The effects of non-linearity
Taguchi’s designs
Quality by design in the pharmaceutical industry
Introduction to quality by design
A quality by design case study – the full factorial design
A quality by design case study – the profiler and desirability function
A quality by design case study – the design space
Tolerance designs
More case studies
The Quinlan experiment at Flex Products, Inc
Computer response time optimization
Chapter highlights
ExercisesComputer Experiments
Introduction to computer experiments
Designing computer experiments
Analyzing computer experiments
Stochastic emulators
Integrating physical and computer experiments
Chapter highlights
ExercisesReliability and survival analysis
Reliability Analysis
Basic notions
Time categories
Reliability and related functions
System reliability
Availability of repairable systems
Types of observations on Ttf
graphical analysis of life data
Non-parametric estimation of reliability
Estimation of life characteristics
Maximum likelihood estimators for exponential Ttf distribution
Maximum likelihood estimation of the Weibull parameters
Reliability demonstration
Binomial testing
Exponential distributions
Accelerated life testing
The Arrhenius temperature model
Other models
Burn-in procedures
Chapter highlights
ExercisesBayesian Reliability Estimation and Prediction
Prior and posterior distributions
Loss functions and Bayes estimators
Distribution-free Bayes estimator of reliability
Bayes estimator of reliability for exponential life distributions
Bayesian credibility and prediction intervals
Distribution-free reliability estimation
Exponential reliability estimation
Prediction intervals
Credibility intervals for the asymptotic availability of repairable systems: The exponential case
Empirical Bayes method
Chapter highlights
ExercisesList of R packages
References and Further Reading
Author Index
Subject Index
Also available on book’s website: wwwwileycom/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 (mistatpdf) and List of R scripts, by Chapter (R_scriptszip)
Appendix Iv: source Version of mistat Package (mistat_targz), 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