Modelling Quality with R

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


This chapter provides the necessary background to understand the fundamental ideas of descriptive and inferential statistics. In particular, the basic ideas and tools used in the description both graphical and numerical, of the inherent variability always present in real world are described. Additionally, some of the most usual statistical distributions used in quality control, for both the discrete and the continuous domains are introduced. Finally, the very important topic of statistical inference contains many examples of specific applications of R to solve these problems. The chapter also summarizes a selection of the ISO standards available to help users in the practice of descriptive and inferential statistic problems.


Central Limit Theorem Control Chart Discrete Distribution Quantile Function Hypergeometric Distribution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Cano, E.L., Moguerza, J.M., Redchuk, A.: Six Sigma with R. In: Statistical Engineering for Process Improvement. Use R!, vol. 36. Springer, New York (2012).
  2. 2.
    Chen, Z.: A note on the runs test. Model Assist. Stat. Appl. 5, 73–77 (2010)Google Scholar
  3. 3.
    Hsu, H.: Shaum’s Outline of Probability, Random Variables and Random Processes. Shaum’s Outline Series, 2nd edn. McGraw-Hill, New York (2010)Google Scholar
  4. 4.
    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).
  5. 5.
    ISO TC69/SCS–Secretariat: ISO 16269-4:2010 - Statistical interpretation of data – Part 4: Detection and treatment of outliers. Published standard (2010).
  6. 6.
    ISO TC69/SCS–Secretariat: ISO 11453:1996 - Statistical interpretation of data – Tests and confidence intervals relating to proportions. Published standard (2012).
  7. 7.
    ISO TC69/SCS–Secretariat: ISO 5479:1997 - Statistical interpretation of data – Tests for departure from the normal distribution. Published standard (2012).
  8. 8.
    ISO TC69/SCS–Secretariat: ISO 2602:1980 - Statistical interpretation of test results – Estimation of the mean – Confidence interval. Published standard (2015).
  9. 9.
    ISO TC69/SCS–Secretariat: ISO 2854:1976 - Statistical interpretation of data – Techniques of estimation and tests relating to means and variances. Published standard (2015).
  10. 10.
    ISO TC69/SCS–Secretariat: ISO 3301:1975 - Statistical interpretation of data – Comparison of two means in the case of paired observations. Published standard (2015).
  11. 11.
    ISO TC69/SCS–Secretariat: ISO 3494:1976 - Statistical interpretation of data – Power of tests relating to means and variances. Published standard (2015).
  12. 12.
    Montgomery, D.: Statistical Quality Control, 7th edn. Wiley, New York (2012)Google Scholar
  13. 13.
    Rumsey, D.: Statistics For Dummies. Wiley, New York (2011)Google Scholar
  14. 14.
    Sarkar, D.: Lattice: Multivariate Data Visualization with R. Springer, New York (2008). ISBN 978-0-387-75968-5
  15. 15.
    Schilling, M.F.: The surprising predictability of long runs. Math. Mag. 85, 141–149 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Sturges, H.A.: The choice of a class interval. J. Am. Stat. Assoc. 21, 65–66 (1926)CrossRefGoogle 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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