Cover -- Title Page -- Copyright -- Contents -- Preface to Second Edition -- Preface to First Edition -- Abbreviations -- Part I Principles of Statistical Thinking and Analysis -- Chapter 1 The Role of Statistical Methods in Modern Industry and Services -- 1.1 The different functional areas in industry and services -- 1.2 The quality-productivity dilemma -- 1.3 Fire-fighting -- 1.4 Inspection of products -- 1.5 Process control -- 1.6 Quality by design -- 1.7 Information quality and practical statistical efficiency -- 1.8 Chapter highlights -- 1.9 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 m -- s known.

4.4.2 Confidence intervals for m -- s unknown -- 4.4.3 Confidence intervals for Q2 -- 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 Non-parametric 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 (available for download from Appendix VI of the book website) -- 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 boxplots -- 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: Step-wise 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 -- Part II Acceptance Sampling -- 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 Xn and Sn2 under RSWR -- 6.2.2 Properties of Xn 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 Sampling Plans for Product Inspection -- 7.1 General discussion -- 7.2 Single-stage sampling plans for attributes -- 7.3 Approximate determination of the sampling plan -- 7.4 Double-sampling plans for attributes -- 7.5 Sequential sampling -- 7.6 Acceptance sampling plans for variables.

7.7 Rectifying inspection of lots -- 7.8 National and international standards -- 7.9 Skip-lot sampling plans for attributes -- 7.9.1 The ISO 2859 skip-lot sampling procedures -- 7.10 The Deming inspection criterion -- 7.11 Published tables for acceptance sampling -- 7.12 Chapter highlights -- 7.13 Exercises -- Part III Statistical Process Control -- Chapter 8 Basic Tools and Principles of Process Control -- 8.1 Basic concepts of statistical process control -- 8.2 Driving a process with control charts -- 8.3 Setting up a control chart: Process capability studies -- 8.4 Process capability indices -- 8.5 Seven tools for process control and process improvement -- 8.6 Statistical analysis of Pareto charts -- 8.7 The Shewhart control charts -- 8.7.1 Control charts for attributes -- 8.7.2 Control charts for variables -- 8.8 Chapter highlights -- 8.9 Exercises -- Chapter 9 Advanced Methods of Statistical Process Control -- 9.1 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 -- 9.2 Modified Shewhart control charts for X -- 9.3 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 -- 9.4 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 -- 9.5 Bayesian detection -- 9.6 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 -- 9.7 Automatic process control -- 9.8 Chapter highlights -- 9.9 Exercises.

Chapter 10 Multivariate Statistical Process Control.