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

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