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Core Concepts in Data Analysis: Summarization, Correlation and Visualization pp 67–112Cite as

2D Analysis: Correlation and Visualization of Two Features

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Part of the book series: Undergraduate Topics in Computer Science ((UTICS))

Abstract

The chapter outlines several important characteristics of summarization and correlation between two features, and displays some of the properties of those. They are: linear regression and correlation coefficient for two quantitative variables; tabular regression, correlation ratio, decomposition of the quantitative feature scatter, and nearest neighbor classifier for the mixed scale case; and contingency table, Quetelet index, statistical independence, and Pearson’s chi-squared for two nominal variables; the latter is treated as a summary correlation measure, in contrast to the conventional view of it as a criterion of statistical independence. They all are applicable in the case of multidimensional data as well.

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Authors and Affiliations

  1. Research University – Higher School of Economics, School of Applied Mathematics and Informatics, 11 Pokrovsky Boulevard, Moscow, RF, Russia

    Boris Mirkin

  2. Department of Computer Science, Birkbeck University of London, Malet Street, London, UK

    Boris Mirkin

Authors
  1. Boris Mirkin
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© 2011 Springer-Verlag London Limited

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Mirkin, B. (2011). 2D Analysis: Correlation and Visualization of Two Features. In: Core Concepts in Data Analysis: Summarization, Correlation and Visualization. Undergraduate Topics in Computer Science. Springer, London. https://doi.org/10.1007/978-0-85729-287-2_3

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  • DOI: https://doi.org/10.1007/978-0-85729-287-2_3

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  • Publisher Name: Springer, London

  • Print ISBN: 978-0-85729-286-5

  • Online ISBN: 978-0-85729-287-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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