Abstract
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.
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Notes
- 1.
See the concept of sampling distribution in Chapter 5.
References
Cochran, W.: Sampling Techniques. Wiley Series in Probability and Mathematical Statistics: Applied Probability and Statistics. Wiley, New York (1977)
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). http://www.iso.org/iso/catalogue_detail.htm?csnumber=40145
ISO TC69/SC1–Terminology and Symbols: ISO 3534-2:2006 - Statistics – Vocabulary and symbols – Part 2: Applied statistics. Published standard (2014). http://www.iso.org/iso/catalogue_detail.htm?csnumber=40147
ISO TC69/SC1–Terminology and Symbols: ISO 3534-4:2014 - Statistics – Vocabulary and symbols – Part 4: Survey sampling. Published standard (2014). http://www.iso.org/iso/catalogue_detail.htm?csnumber=56154
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ISO TC69/SCS–Secretariat: ISO 28640:2010 - Random variate generation methods. Published standard (2015). http://www.iso.org/iso/catalogue_detail.htm?csnumber=42333
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Cano, E.L., Moguerza, J.M., Corcoba, M.P. (2015). Data Sampling for Quality Control with R. In: Quality Control with R. Use R!. Springer, Cham. https://doi.org/10.1007/978-3-319-24046-6_6
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DOI: https://doi.org/10.1007/978-3-319-24046-6_6
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