Modern Industrial Statistics - With Applications in R, Minitab and Jmp

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: www.wiley.com/go/modern_industrial_statistics.

List of contents

Preface to Second Edition xvPreface to First Edition xviiAbbreviations xixPART I PRINCIPLES OF STATISTICAL THINKING AND ANALYSIS 11 The Role of Statistical Methods in Modern Industry and Services 31.1 The different functional areas in industry and services 31.2 The quality-productivity dilemma 51.3 Fire-fighting 61.4 Inspection of products 71.5 Process control 71.6 Quality by design 81.7 Information quality and practical statistical efficiency 91.8 Chapter highlights 111.9 Exercises 122 Analyzing Variability: Descriptive Statistics 132.1 Random phenomena and the structure of observations 132.2 Accuracy and precision of measurements 172.3 The population and the sample 182.4 Descriptive analysis of sample values 192.5 Prediction intervals 322.6 Additional techniques of exploratory data analysis 322.7 Chapter highlights 382.8 Exercises 383 Probability Models and Distribution Functions 413.1 Basic probability 413.2 Random variables and their distributions 513.3 Families of discrete distribution 603.4 Continuous distributions 693.5 Joint, marginal and conditional distributions 823.6 Some multivariate distributions 883.7 Distribution of order statistics 923.8 Linear combinations of random variables 943.9 Large sample approximations 983.10 Additional distributions of statistics of normal samples 1013.11 Chapter highlights 1043.12 Exercises 1054 Statistical Inference and Bootstrapping 1134.1 Sampling characteristics of estimators 1134.2 Some methods of point estimation 1144.3 Comparison of sample estimates 1204.4 Confidence intervals 1284.5 Tolerance intervals 1324.6 Testing for normality with probability plots 1344.7 Tests of goodness of fit 1374.8 Bayesian decision procedures 1404.9 Random sampling from reference distributions 1484.10 Bootstrap sampling 1504.11 Bootstrap testing of hypotheses 1524.12 Bootstrap tolerance intervals 1614.13 Non-parametric tests 1654.14 Description of MINITAB macros (available for download from Appendix VI of the book website) 1704.15 Chapter highlights 1704.16 Exercises 1715 Variability in Several Dimensions and Regression Models 1775.1 Graphical display and analysis 1775.2 Frequency distributions in several dimensions 1815.3 Correlation and regression analysis 1855.4 Multiple regression 1925.5 Partial regression and correlation 1985.6 Multiple linear regression 2005.7 Partial F-tests and the sequential SS 2045.8 Model construction: Step-wise regression 2065.9 Regression diagnostics 2095.10 Quantal response analysis: Logistic regression 2115.11 The analysis of variance: The comparison of means 2135.12 Simultaneous confidence intervals: Multiple comparisons 2165.13 Contingency tables 2205.14 Categorical data analysis 2275.15 Chapter highlights 2295.16 Exercises 230PART II ACCEPTANCE SAMPLING 2356 Sampling for Estimation of Finite Population Quantities 2376.1 Sampling and the estimation problem 2376.2 Estimation with simple random samples 2416.3 Estimating the mean with stratified RSWOR 2486.4 Proportional and optimal allocation 2496.5 Prediction models with known covariates 2526.6 Chapter highlights 2556.7 Exercises 2567 Sampling Plans for Product Inspection 2587.1 General discussion 2587.2 Single-stage sampling plans for attributes 2597.3 Approximate determination of the sampling plan 2627.4 Double-sampling plans for attributes 2647.5 Sequential sampling 2677.6 Acceptance sampling plans for variables 2707.7 Rectifying inspection of lots 2727.8 National and international standards 2747.9 Skip-lot sampling plans for attributes 2767.10 The Deming inspection criterion 2787.11 Published tables for acceptance sampling 2797.12 Chapter highlights 2807.13 Exercises 281PART III STATISTICAL PROCESS CONTROL 2838 Basic Tools and Principles of Process Control 2858.1 Basic concepts of statistical process control 2858.2 Driving a process with control charts 2948.3 Setting up a control chart: Process capability studies 2988.4 Process capability indices 3008.5 Seven tools for process control and process improvement 3028.6 Statistical analysis of Pareto charts 3058.7 The Shewhart control charts 3088.8 Chapter highlights 3168.9 Exercises 3169 Advanced Methods of Statistical Process Control 3199.1 Tests of randomness 3199.2 Modified Shewhart control charts for X 3259.3 The size and frequency of sampling for Shewhart control charts 3289.4 Cumulative sum control charts 3309.5 Bayesian detection 3429.6 Process tracking 3469.7 Automatic process control 3549.8 Chapter highlights 3569.9 Exercises 35710 Multivariate Statistical Process Control 36110.1 Introduction 36110.2 A review of multivariate data analysis 36510.3 Multivariate process capability indices 36710.4 Advanced applications of multivariate control charts 37010.5 Multivariate tolerance specifications 37410.6 Chapter highlights 37610.7 Exercises 377PART IV DESIGN AND ANALYSIS OF EXPERIMENTS 37911 Classical Design and Analysis of Experiments 38111.1 Basic steps and guiding principles 38111.2 Blocking and randomization 38511.3 Additive and non-additive linear models 38511.4 The analysis of randomized complete block designs 38711.5 Balanced incomplete block designs 39411.6 Latin square design 39711.7 Full factorial experiments 40211.8 Blocking and fractional replications of 2m factorial designs 42511.9 Exploration of response surfaces 43011.10 Chapter highlights 44111.11 Exercises 44212 Quality by Design 44612.1 Off-line quality control, parameter design and the Taguchi method 44712.2 The effects of non-linearity 45212.3 Taguchi's designs 45612.4 Quality by design in the pharmaceutical industry 45812.5 Tolerance designs 46212.6 More case studies 46712.7 Chapter highlights 47412.8 Exercises 47413 Computer Experiments 47713.1 Introduction to computer experiments 47713.2 Designing computer experiments 48113.3 Analyzing computer experiments 48313.4 Stochastic emulators 48813.5 Integrating physical and computer experiments 49113.6 Chapter highlights 49213.7 Exercises 492PART V RELIABILITY AND SURVIVAL ANALYSIS 49514 Reliability Analysis 49714.1 Basic notions 49814.2 System reliability 50014.3 Availability of repairable systems 50314.4 Types of observations on TTF 50914.5 Graphical analysis of life data 51014.6 Non-parametric estimation of reliability 51314.7 Estimation of life characteristics 51414.8 Reliability demonstration 52014.9 Accelerated life testing 52814.10 Burn-in procedures 52914.11 Chapter highlights 53014.12 Exercises 53115 Bayesian Reliability Estimation and Prediction 53415.1 Prior and posterior distributions 53415.2 Loss functions and Bayes estimators 53715.3 Bayesian credibility and prediction intervals 53915.4 Credibility intervals for the asymptotic availability of repairable systems: The exponential case 54215.5 Empirical Bayes method 54315.6 Chapter highlights 54515.7 Exercises 545List of R Packages 547References and Further Reading 549Author Index 555Subject Index 557Also available on book's website: www.wiley.com/go/modern_industrial_statisticsAppendix I: An Introduction to R by Stefano IacusAppendix II: Basic MINITAB Commands and a Review of Matrix Algebra for StatisticsAppendix III: mistat Manual (mistat.pdf) and List of R Scripts, by Chapter (R_scripts.zip)Appendix IV: Source Version of mistat Package (mistat_1.0.tar.gz), also available on theComprehensive R Archive Network (CRAN) Website.Appendix V: Data Sets as csv FilesAppendix VI: MINITAB MacrosAppendix VII: JMP Scripts by Ian CoxAppendix VIII: Solution Manual

About the author

RON S. KENETT, The KPA Group, Israel, University of Turin, Italy and NYU Center for Risk Engineering, New York, USA

SHELEMYAHU ZACKS, Binghamton University, Binghamton, USA

With contributions from DANIELE AMBERTI, Turin, Italy

Summary

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: www.wiley.com/go/modern_industrial_statistics.

Report

"This book delivers on its promise of providing a theoretical, practical, and computer-based approach to industrial statistics." ( Journal of Quality Technology , 1 October 2014)

Product details

• Authors: Daniele Amberti, Ro Kenett, Ron Kenett, Ron S. Kenett, Ron S. Zacks Kenett, Ron Zacks Kenett, Shelemyah Zacks, Shelemyahu Zacks
• Publisher: Wiley, John and Sons Ltd
• Languages: English
• Product format: Hardback
• Released: 24.01.2014
• EAN: 9781118456064
• ISBN: 978-1-118-45606-4
• No. of pages: 592
• Series: Statistics in Practice; Statistics in Practice
• Subjects: Natural sciences, medicine, IT, technology > Mathematics > Probability theory, stochastic theory, mathematical statistics; Statistik, Statistics, Electrical & Electronics Engineering, Elektrotechnik u. Elektronik, Statistik in den Ingenieurwissenschaften, Engineering Statistics, Quality & Reliability, Qualität u. Zuverlässigkeit, Statistics for Finance, Business & Economics, Finanz- u. Wirtschaftsstatistik