Abstract
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.
Keywords
- 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.
This is a preview of subscription content, access via your institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
The data frame is also available in the SixSigma package.
- 2.
Actually, a version of those quartiles called hinches, see [5] and?boxplot.stats.
- 3.
A normal distribution with μ = 0 and σ = 1.
References
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). http://www.springer.com/statistics/book/978-1-4614-3651-5
Chen, Z.: A note on the runs test. Model Assist. Stat. Appl. 5, 73–77 (2010)
Hsu, H.: Shaum’s Outline of Probability, Random Variables and Random Processes. Shaum’s Outline Series, 2nd edn. McGraw-Hill, New York (2010)
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/SCS–Secretariat: ISO 16269-4:2010 - Statistical interpretation of data – Part 4: Detection and treatment of outliers. Published standard (2010). http://www.iso.org/iso/catalogue_detail.htm?csnumber=44396
ISO TC69/SCS–Secretariat: ISO 11453:1996 - Statistical interpretation of data – Tests and confidence intervals relating to proportions. Published standard (2012). http://www.iso.org/iso/catalogue_detail.htm?csnumber=19405
ISO TC69/SCS–Secretariat: ISO 5479:1997 - Statistical interpretation of data – Tests for departure from the normal distribution. Published standard (2012). http://www.iso.org/iso/catalogue_detail.htm?csnumber=22506
ISO TC69/SCS–Secretariat: ISO 2602:1980 - Statistical interpretation of test results – Estimation of the mean – Confidence interval. Published standard (2015). http://www.iso.org/iso/catalogue_detail.htm?csnumber=7585
ISO TC69/SCS–Secretariat: ISO 2854:1976 - Statistical interpretation of data – Techniques of estimation and tests relating to means and variances. Published standard (2015). http://www.iso.org/iso/catalogue_detail.htm?csnumber=7854
ISO TC69/SCS–Secretariat: ISO 3301:1975 - Statistical interpretation of data – Comparison of two means in the case of paired observations. Published standard (2015). http://www.iso.org/iso/catalogue_detail.htm?csnumber=8540
ISO TC69/SCS–Secretariat: ISO 3494:1976 - Statistical interpretation of data – Power of tests relating to means and variances. Published standard (2015). http://www.iso.org/iso/catalogue_detail.htm?csnumber=8845
Montgomery, D.: Statistical Quality Control, 7th edn. Wiley, New York (2012)
Rumsey, D.: Statistics For Dummies. Wiley, New York (2011)
Sarkar, D.: Lattice: Multivariate Data Visualization with R. Springer, New York (2008). http://lmdvr.r-forge.r-project.org. ISBN 978-0-387-75968-5
Schilling, M.F.: The surprising predictability of long runs. Math. Mag. 85, 141–149 (2012)
Sturges, H.A.: The choice of a class interval. J. Am. Stat. Assoc. 21, 65–66 (1926)
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Cano, E.L., Moguerza, J.M., Corcoba, M.P. (2015). Modelling Quality with R. In: Quality Control with R. Use R!. Springer, Cham. https://doi.org/10.1007/978-3-319-24046-6_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-24046-6_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-24044-2
Online ISBN: 978-3-319-24046-6
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)