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Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 176))

Abstract

The field of statistics deals with collecting, exploring and presenting large amounts of data to discover underlying patterns and trends. Statistics is applied every day in many areas of our lives: business, industry, medicine and government—to facilitate making informed decisions in the presence of uncertainty and variation. For example, factory authorities use information from statistical quality control unit to know whether the length or weight of their products is within established standards. In this chapter, we present the basic steps of performing such statistical study which relies on employing a small, representative sample of products to make statistical inference of a wider population. We first describe methods of descriptive statistics which enable to capture the important features from sample data using tables, charts and graphs. Then, the described sampling distributions pave the way to inferential statistics, which allows us to draw conclusions about the whole population from which we took the sample. We describe here the basics of such statistical inference: parametric and nonparametric methods of estimation and hypothesis testing. Also some examples of non-classical statistical methods are presented.

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Bibliography

  • Hodges Jr., J.L., Lehmann, E.L.: Basic Concepts of Probability and Statistics, 2nd edn. Society for Industrial and Applied Mathematics, Philadelphia (2005)

    Book  Google Scholar 

  • R Core Team: R language definition. http://cran.r-project.org/doc/manuals/r-release/R-lang.pdf (2019). Accessed 16 Dec 2019

  • Rice, J.: Mathematical Statistics and Data Analysis, 3rd edn. Thomson-Brooks/Cole, Belmont (2007)

    Google Scholar 

  • Tukey, J.W.: Exploratory Data Analysis. Addison-Wesley Publishing Co., Reading (1977)

    Google Scholar 

  • Walpole, R.E., Myers, R.H., Myers, S.L., Ye, K.: Probability and Statistics for Engineers and Scientists, 9th edn. Pearson Education, Essex (2016)

    MATH  Google Scholar 

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Correspondence to Katarzyna Stapor .

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Stapor, K. (2020). Descriptive and Inferential Statistics. In: Introduction to Probabilistic and Statistical Methods with Examples in R . Intelligent Systems Reference Library, vol 176. Springer, Cham. https://doi.org/10.1007/978-3-030-45799-0_2

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  • DOI: https://doi.org/10.1007/978-3-030-45799-0_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-45798-3

  • Online ISBN: 978-3-030-45799-0

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