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

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Cover Title Page Copyright Contents Preface to Third Edition Preface to Second Edition Preface to First Edition List of Abbreviations Part I Modern Statistics: A Computer‐Based Approach Chapter 1 Statistics and Analytics in Modern Industry 1.1 Analytics, big data, and the fourth industrial revolution 1.2 Computer age analytics 1.3 The analytics maturity ladder 1.4 Information quality 1.5 Chapter highlights 1.6 Exercises Chapter 2 Analyzing Variability: Descriptive Statistics 2.1 Random phenomena and the structure of observations 2.2 Accuracy and precision of measurements 2.3 The population and the sample 2.4 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 2.5 Prediction intervals 2.6 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 2.7 Chapter highlights 2.8 Exercises Chapter 3 Probability Models and Distribution Functions 3.1 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 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 Chapter 4 Statistical Inference and Bootstrapping 4.1 Sampling characteristics of estimators 4.2 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 4.3 Comparison of sample estimates 4.3.1 Basic concepts 4.3.2 Some common one‐sample tests of hypotheses 4.4 Confidence intervals 4.4.1 Confidence intervals for μ; σ known 4.4.2 Confidence intervals for μ; σ unknown 4.4.3 Confidence intervals for σ2 4.4.4 Confidence intervals for p 4.5 Tolerance intervals 4.5.1 Tolerance intervals for the normal distributions 4.6 Testing for normality with probability plots 4.7 Tests of goodness of fit 4.7.1 The chi‐square test (large samples) 4.7.2 The Kolmogorov–Smirnov test 4.8 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 4.9 Random sampling from reference distributions 4.10 Bootstrap sampling 4.10.1 The bootstrap method 4.10.2 Examining the bootstrap method 4.10.3 Harnessing the bootstrap method 4.11 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 4.12 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 4.13 Nonparametric tests 4.13.1 The sign test 4.13.2 The randomization test 4.13.3 The Wilcoxon signed rank test 4.14 Description of MINITAB macros 4.15 Chapter highlights 4.16 Exercises Chapter 5 Variability in Several Dimensions and Regression Models 5.1 Graphical display and analysis 5.1.1 Scatterplots 5.1.2 Multiple box‐plots 5.2 Frequency distributions in several dimensions 5.2.1 Bivariate joint frequency distributions 5.2.2 Conditional distributions 5.3 Correlation and regression analysis 5.3.1 Covariances and correlations 5.3.2 Fitting simple regression lines to data 5.4 Multiple regression 5.4.1 Regression on two variables 5.5 Partial regression and correlation 5.6 Multiple linear regression 5.7 Partial F‐tests and the sequential SS 5.8 Model construction: stepwise regression 5.9 Regression diagnostics 5.10 Quantal response analysis: logistic regression 5.11 The analysis of variance: the comparison of means 5.11.1 The statistical model 5.11.2 The one‐way analysis of variance (ANOVA) 5.12 Simultaneous confidence intervals: multiple comparisons 5.13 Contingency tables 5.13.1 The structure of contingency tables 5.13.2 Indices of association for contingency tables 5.14 Categorical data analysis 5.14.1 Comparison of binomial experiments 5.15 Chapter highlights 5.16 Exercises Chapter 6 Sampling for Estimation of Finite Population Quantities 6.1 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 6.2 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 6.3 Estimating the mean with stratified RSWOR 6.4 Proportional and optimal allocation 6.5 Prediction models with known covariates 6.6 Chapter highlights 6.7 Exercises Chapter 7 Time Series Analysis and Prediction 7.1 The components of a time series 7.1.1 The trend and covariances 7.1.2 Applications with MINITAB and JMP 7.2 Covariance stationary time series 7.2.1 Moving averages 7.2.2 Auto‐regressive time series 7.2.3 Auto‐regressive moving averages time series 7.2.4 Integrated auto‐regressive moving average time series 7.2.5 Applications with JMP and R 7.3 Linear predictors for covariance stationary time series 7.3.1 Optimal linear predictors 7.4 Predictors for nonstationary time series 7.4.1 Quadratic LSE predictors 7.4.2 Moving average smoothing predictors 7.5 Dynamic linear models 7.5.1 Some special cases 7.6 Chapter highlights 7.7 Exercises Chapter 8 Modern Analytic Methods 8.1 Introduction to computer age statistics 8.2 Decision trees 8.3 Naïve Bayes classifier 8.4 Clustering methods 8.5 Functional data analysis 8.6 Text analytics 8.7 Chapter highlights 8.8 Exercises Part II Modern Industrial Statistics: Design and Control of Quality and Reliability Chapter 9 The Role of Statistical Methods in Modern Industry and Services 9.1 The different functional areas in industry and services 9.2 The quality‐productivity dilemma 9.3 Firefighting 9.4 Inspection of products 9.5 Process control 9.6 Quality by design 9.7 Practical statistical efficiency 9.8 Chapter highlights 9.9 Exercises Chapter 10 Basic Tools and Principles of Process Control 10.1 Basic concepts of statistical process control 10.2 Driving a process with control charts 10.3 Setting up a control chart: process capability studies 10.4 Process capability indices 10.5 Seven tools for process control and process improvement 10.5.1 Flow charts 10.5.2 Check sheets 10.5.3 Run charts 10.5.4 Histograms 10.5.5 Pareto charts 10.5.6 Scatterplots 10.5.7 Cause and effect diagrams 10.6 Statistical analysis of Pareto charts 10.7 The Shewhart control charts 10.7.1 Control charts for attributes 10.7.2 Control charts for variables 10.8 Chapter highlights 10.9 Exercises Chapter 11 Advanced Methods of Statistical Process Control 11.1 Tests of randomness 11.1.1 Testing the number of runs 11.1.2 Runs above and below a specified level 11.1.3 Runs up and down 11.1.4 Testing the length of runs up and down 11.2 Modified Shewhart control charts for X‾ 11.3 The size and frequency of sampling for Shewhart control charts 11.3.1 The economic design for X‾‐charts 11.3.2 Increasing the sensitivity of p‐charts 11.4 Cumulative sum control charts 11.4.1 Upper Page's scheme 11.4.2 Some theoretical background 11.4.3 Lower and two‐sided Page's scheme 11.4.4 Average run length, probability of false alarm, and conditional expected delay 11.5 Bayesian detection 11.6 Process tracking 11.6.1 The EWMA procedure 11.6.2 The BECM procedure 11.6.3 The Kalman filter 11.6.4 Hoadley's QMP 11.7 Automatic process control 11.8 Chapter highlights 11.9 Exercises Chapter 12 Multivariate Statistical Process Control 12.1 Introduction 12.2 A review multivariate data analysis 12.3 Multivariate process capability indices 12.4 Advanced applications of multivariate control charts 12.4.1 Multivariate control charts scenarios 12.4.2 Internally derived targets 12.4.3 Using an external reference sample 12.4.4 Externally assigned targets 12.4.5 Measurement units considered as batches 12.4.6 Variable decomposition and monitoring indices 12.5 Multivariate tolerance specifications 12.6 Chapter highlights 12.7 Exercises Chapter 13 Classical Design and Analysis of Experiments 13.1 Basic steps and guiding principles 13.2 Blocking and randomization 13.3 Additive and nonadditive linear models 13.4 The analysis of randomized complete block designs 13.4.1 Several blocks, two treatments per block: paired comparison 13.4.2 Several blocks, t treatments per block 13.5 Balanced incomplete block designs 13.6 Latin square design 13.7 Full factorial experiments 13.7.1 The structure of factorial experiments 13.7.2 The ANOVA for full factorial designs 13.7.3 Estimating main effects and interactions 13.7.4 2m factorial designs 13.7.5 3m factorial designs 13.8 Blocking and fractional replications of 2m factorial designs 13.9 Exploration of response surfaces 13.9.1 Second‐order designs 13.9.2 Some specific second‐order designs 13.9.3 Approaching the region of the optimal yield 13.9.4 Canonical representation 13.10 Chapter highlights 13.11 Exercises Chapter 14 Quality by Design 14.1 Off‐line quality control, parameter design, and the Taguchi method 14.1.1 Product and process optimization using loss functions 14.1.2 Major stages in product and process design 14.1.3 Design parameters and noise factors 14.1.4 Parameter design experiments 14.1.5 Performance statistics 14.2 The effects of non‐linearity 14.3 Taguchi's designs 14.4 Quality by design in the pharmaceutical industry 14.4.1 Introduction to quality by design 14.4.2 A quality by design case study – the full factorial design 14.4.3 A quality by design case study – the profiler and desirability function 14.4.4 A quality by design case study – the design space 14.5 Tolerance designs 14.6 Case studies 14.6.1 The Quinlan experiment 14.6.2 Computer response time optimization 14.7 Chapter highlights 14.8 Exercises Chapter 15 Computer Experiments 15.1 Introduction to computer experiments 15.2 Designing computer experiments 15.3 Analyzing computer experiments 15.4 Stochastic emulators 15.5 Integrating physical and computer experiments 15.6 Simulation of random variables 15.6.1 Basic procedures 15.6.2 Generating random vectors 15.6.3 Approximating integrals 15.7 Chapter highlights 15.8 Exercises Chapter 16 Reliability Analysis 16.1 Basic notions 16.1.1 Time categories 16.1.2 Reliability and related functions 16.2 System reliability 16.3 Availability of repairable systems 16.4 Types of observations on TTF 16.5 Graphical analysis of life data 16.6 Nonparametric estimation of reliability 16.7 Estimation of life characteristics 16.7.1 Maximum likelihood estimators for exponential TTF distribution 16.7.2 Maximum likelihood estimation of the Weibull parameters 16.8 Reliability demonstration 16.8.1 Binomial testing 16.8.2 Exponential distributions 16.9 Accelerated life testing 16.9.1 The Arrhenius temperature model 16.9.2 Other models 16.10 Burn‐in procedures 16.11 Chapter highlights 16.12 Exercises Chapter 17 Bayesian Reliability Estimation and Prediction 17.1 Prior and posterior distributions 17.2 Loss functions and bayes estimators 17.2.1 Distribution‐free bayes estimator of reliability 17.2.2 Bayes estimator of reliability for exponential life distributions 17.3 Bayesian credibility and prediction intervals 17.3.1 Distribution‐free reliability estimation 17.3.2 Exponential reliability estimation 17.3.3 Prediction intervals 17.3.4 Applications with JMP 17.4 Credibility intervals for the asymptotic availability of repairable systems: the exponential case 17.5 Empirical bayes method 17.6 Chapter highlights 17.7 Exercises Chapter 18 Sampling Plans for Batch and Sequential Inspection 18.1 General discussion 18.2 Single‐stage sampling plans for attributes 18.3 Approximate determination of the sampling plan 18.4 Double‐sampling plans for attributes 18.5 Sequential A/B testing 18.5.1 The one‐armed Bernoulli bandits 18.5.2 Two‐armed Bernoulli bandits 18.6 Acceptance sampling plans for variables 18.7 Rectifying inspection of lots 18.8 National and international standards 18.9 Skip‐lot sampling plans for attributes 18.9.1 The ISO 2859 skip‐lot sampling procedures 18.10 The Deming inspection criterion 18.11 Published tables for acceptance sampling 18.12 Chapter highlights 18.13 Exercises List of R Packages Solution Manual References Author Index Subject Index EULA