LEADER 05809nam 2200757 450 001 9910790863703321 005 20220927192350.0 010 $a1-118-76369-6 010 $a1-118-76366-1 010 $a1-118-76368-8 035 $a(CKB)2550000001163225 035 $a(EBL)1563648 035 $a(OCoLC)863824734 035 $a(SSID)ssj0001061366 035 $a(PQKBManifestationID)11550897 035 $a(PQKBTitleCode)TC0001061366 035 $a(PQKBWorkID)11098839 035 $a(PQKB)11371825 035 $a(MiAaPQ)EBC1563648 035 $a(DLC) 2013032068 035 $a(Au-PeEL)EBL1563648 035 $a(CaPaEBR)ebr10809691 035 $a(CaONFJC)MIL545363 035 $a(PPN)192205013 035 $a(EXLCZ)992550000001163225 100 $a20131209d2014 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aModern industrial statistics $ewith applications in R, MINITAB and JMP /$fRon S. Kenett, Shelemyahu Zacks ; with contributions from Daniele Amberti 205 $aSecond edition. 210 1$aChichester, England :$cWiley,$d2014. 210 4$dİ2014 215 $a1 online resource (587 p.) 225 0 $aStatistics in practice 300 $aDescription based upon print version of record. 311 $a1-118-45606-8 311 $a1-306-14112-5 320 $aIncludes bibliographical references and indexes. 327 $aCover; 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 327 $a2.1 Random phenomena and the structure of observations2.2 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 327 $a2.7 Chapter highlights2.8 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 327 $a3.2.3 The standard deviation, quantiles, measures of skewness and kurtosis3.2.4 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 327 $a3.5 Joint, marginal and conditional distributions3.5.1 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 327 $a3.10.1 Distribution of the sample variance 330 $aFully revised and updated, this book combines a theoretical background with examples and references to R, MINITAB and JMP, enabling practitioners to find state-of-the-art material on both foundation and implementation tools to support their work. Topics addressed include computer-intensive data analysis, acceptance sampling, univariate and multivariate statistical process control, design of experiments, quality by design, and reliability using classical and Bayesian methods. The book can be used for workshops or courses on acceptance sampling, statistical process control, design of experime 410 0$aStatistics in Practice 606 $aQuality control$xStatistical methods 606 $aReliability (Engineering)$xStatistical methods 606 $aR (Computer program language) 615 0$aQuality control$xStatistical methods. 615 0$aReliability (Engineering)$xStatistical methods. 615 0$aR (Computer program language) 676 $a658.5/62 700 $aKenett$b Ron$0874200 701 $aZacks$b Shelemyahu$f1932-$012451 701 $aAmberti$b Daniele$01547452 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910790863703321 996 $aModern industrial statistics$93803850 997 $aUNINA