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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References
Carpenter, J., Bithell, J.: Bootstrap confidence intervals: when, which, what? A practical guide for medical statisticians. Stat. Med. 19, 1141–1164 (2000)
Davison, A.C., Hinkley, D.V.: Bootstrap Methods and Their Application. Cambridge University Press, Cambridge (7th printing) (2005)
Kendall, M.G., Stewart, A.: Advanced Statistics: Inference and Relationship, 3d edn. Griffin, London, ISBN: 0852642156 (1973)
Lohninger, H.: Teach Me Data Analysis. Springer, Berlin-New York-Tokyo, ISBN 3-540-14743-8 (1999)
Mirkin, B.: Eleven ways to look at the chi-squared coefficient for contingency tables. Am. Stat. 55(2), 111–120 (2001)
Pearson, K.: On a criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen in random sampling. Phil. Mag. 50, 157–175 (1900)
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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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