Data Sampling for Quality Control with R

  • Emilio L. Cano
  • Javier M. Moguerza
  • Mariano Prieto Corcoba
Part of the Use R! book series (USE R)


Statistical Quality Control tries to predict the behavior of a given process through the collection of a subset of data coming from the performance of the process. This chapter showcases the importance of sampling and describes the most important techniques used to draw representative samples. An example using R on how to plot Operating Characteristic (OC) curves and its application to determine the sample size of groups within a sampling process is shown. Finally, the ISO Standards related to sampling are summarized.


Control Chart Control Limit Simple Random Sampling Uniform Random Variate Real Random Variate 
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  1. 1.
    Cochran, W.: Sampling Techniques. Wiley Series in Probability and Mathematical Statistics: Applied Probability and Statistics. Wiley, New York (1977)Google Scholar
  2. 2.
    ISO TC69/SC1–Terminology and Symbols: ISO 3534-1:2006 - Statistics – Vocabulary and symbols – Part 1: General statistical terms and terms used in probability. Published standard (2010).
  3. 3.
    ISO TC69/SC1–Terminology and Symbols: ISO 3534-2:2006 - Statistics – Vocabulary and symbols – Part 2: Applied statistics. Published standard (2014).
  4. 4.
    ISO TC69/SC1–Terminology and Symbols: ISO 3534-4:2014 - Statistics – Vocabulary and symbols – Part 4: Survey sampling. Published standard (2014).
  5. 5.
    ISO TC69/SC4–Applications of statistical methods in process management: ISO 11462-1:2010 - Guidelines for implementation of statistical process control (SPC) – Part 2: Catalogue of tools and techniques. Published standard (2010).
  6. 6.
    ISO TC69/SC4–Applications of statistical methods in process management: ISO 7870-2:2013 - Control charts – Part 2: Shewhart control charts. Published standard (2013).
  7. 7.
    ISO TC69/SC5–Acceptance sampling: ISO 24153:2009 - Random sampling and randomization procedures. Published standard (2015).
  8. 8.
    ISO TC69/SCS–Secretariat: ISO 28640:2010 - Random variate generation methods. Published standard (2015).
  9. 9.
    Lohr, S.: Sampling: Design and Analysis. Advanced (Cengage Learning). Cengage Learning, Boston (2009)Google Scholar
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    Montgomery, D.: Statistical Quality Control, 7th edn. Wiley Global Education, Hoboken (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Emilio L. Cano
    • 1
    • 2
  • Javier M. Moguerza
    • 1
  • Mariano Prieto Corcoba
    • 3
  1. 1.Department of Computer Science and StatisticsRey Juan Carlos UniversityMadridSpain
  2. 2.Statistics Area, DHEPThe University of Castilla-La ManchaCiudad RealSpain
  3. 3.ENUSA Industrias AvanzadasMadridSpain

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