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

Modern Industrial Statistics: With Applications in R, MINITAB, and JMP
PDF
  • eBook:
    Modern Industrial Statistics: With Applications in R, MINITAB, and JMP
  • Author:
    Ron S. Kenett, Shelemyahu Zacks
  • Edition:
    1 edition
  • Categories:
  • Data:
    May 10, 2021
  • ISBN:
    1119714907
  • ISBN-13:
    9781119714903
  • Language:
    English
  • Pages:
    880 pages
  • Format:
    PDF

Book Description
The new edition of the prime reference on the tools of statistics used in industry and services, integrating theoretical, practical, and computer-based approaches
Modern Industrial Statistics is a leading reference and guide to the statistics tools widely used in industry and services. Designed to help professionals and students easily access relevant theoretical and practical information in a single volume, this standard resource employs a computer-intensive approach to industrial statistics and provides numerous examples and procedures in the popular R language and for MINITAB and JMP statistical analysis software. Divided into two parts, the text covers the principles of statistical thinking and analysis, bootstrapping, predictive analytics, Bayesian inference, time series analysis, acceptance sampling, statistical process control, design and analysis of experiments, simulation and computer experiments, and reliability and survival analysis. Part A, on computer age statistical analysis, can be used in general courses on analytics and statistics. Part B is focused on industrial statistics applications.
The fully revised third edition covers the latest techniques in R, MINITAB and JMP, and features brand-new coverage of time series analysis, predictive analytics and Bayesian inference. New and expanded simulation activities, examples, and case studies—drawn from the electronics, metal work, pharmaceutical, and financial industries—are complemented by additional computer and modeling methods. Helping readers develop skills for modeling data and designing experiments, this comprehensive volume:
  • Explains the use of computer-based methods such as bootstrapping and data visualization
  • Covers nonstandard techniques and applications of industrial statistical process control (SPC) charts
  • Contains numerous problems, exercises, and data sets representing real-life case studies of statistical work in various business and industry settings
  • Includes access to a companion website that contains an introduction to R, sample R code, csv files of all data sets, JMP add-ins, and downloadable appendices
  • Provides an author-created R package, mistat, that includes all data sets and statistical analysis applications used in the book
Part of the acclaimed Statistics in Practice series, Modern Industrial Statistics with Applications in R, MINITAB, and JMP, Third Edition, is the perfect textbook for advanced undergraduate and postgraduate courses in the areas of industrial statistics, quality and reliability engineering, and an important reference for industrial statisticians, researchers, and practitioners in related fields. The mistat R-package is available from the R CRAN repository.

Content

PART I - MODERN STATISTICS: A COMPUTER-BASED APPROACH
1. Statistics and Analytics in Modern Industry
2. Analyzing Variability: Descriptive Statistics
3. Probability Models and Distribution Functions
4. Statistical Inference and Bootstrapping
5. Variability in Several Dimensions and Regression Models
6. Sampling for Estimation of Finite Population Quantities
7. Time Series Analysis and Prediction
8. Modern Analytic Methods

PART II - MODERN INDUSTRIAL STATISTICS: DESIGN AND CONTROL OF QUALITY AND RELIABILITY
9. The Role of Statistical Methods in Modern Industry and Services
10. Basic Tools and Principles of Process Control
11. Advanced Methods of Statistical Process Control
12. Multivariate Statistical Process Control
13. Classical Design and Analysis of Experiments
14. Quality by Design
15. Computer Experiments
16. Reliability Analysis
17. Bayesian Reliability Estimation and Prediction
18. Sampling Plans for Batch and Sequential Inspection

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