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Modern Industrial Statistics : with applications in R, MINITAB and JMP.

Author: Ron Kenett; Shelemyahu Zacks; Daniele Amberti
Publisher: Hoboken : Wiley, 2013.
Series: Statistics in practice.
Edition/Format:   eBook : Document : English : 2nd edView all editions and formats
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  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Kenett, Ron.
Modern Industrial Statistics : with applications in R, MINITAB and JMP.
Hoboken : Wiley, ©2013
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Ron Kenett; Shelemyahu Zacks; Daniele Amberti
ISBN: 9781118763681 1118763688
OCLC Number: 863824734
Notes: 3.10.1 Distribution of the sample variance.
Description: 1 online resource (587 pages).
Contents: 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 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. 2.7 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. 3.2.3 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. 3.5 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.
Series Title: Statistics in practice.

Abstract:

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 experime.

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