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dc.contributor.authorKachapova, F
dc.contributor.authorKachapov, I
dc.date.accessioned2013-06-07T07:27:42Z
dc.date.available2013-06-07T07:27:42Z
dc.date.copyright2010
dc.date.issued2013-06-07
dc.identifier.citationInternational Journal of Mathematical Education in Science and Technology, vol.41(3), pp.418 - 427
dc.identifier.issn0020-739X
dc.identifier.urihttp://hdl.handle.net/10292/5429
dc.description.abstractThe paper introduces a monotony coefficient as a new measure of the monotone dependence in a two-dimensional sample. Some properties of this measure are derived. In particular, it is shown that the absolute value of the monotony coefficient for a twodimensional sample is between | r | and 1, where r is the Pearson’s correlation coefficient for the sample; that the monotony coefficient equals 1 for any monotone increasing sample and equals -1 for any monotone decreasing sample. The paper contains a few examples demonstrating that the monotony coefficient is a more accurate measure of the degree of monotone dependence for a non-linear relationship than the Pearson’s, Spearman’s and Kendall’s correlation coefficients. The monotony coefficient is a tool that can be applied to samples in order to find dependencies between random variables; it is especially useful in finding couples of dependent variables in a big dataset of many variables. Undergraduate students in mathematics and science would benefit from learning and applying this measure of monotone dependence.
dc.publisherTaylor & Francis
dc.relation.urihttp://dx.doi.org/10.1080/00207390903477418
dc.rightsCopyright © 2010 Taylor & Francis. Authors retain the right to place his/her pre-publication version of the work on a personal website or institutional repository as an electronic file for personal or professional use, but not for commercial sale or for any systematic external distribution by a third. This is an electronic version of an article published in (see Citation). International Journal of Mathematical Education in Science and Technology is available online at: www.tandfonline.com with the open URL of your article (see Publisher’s Version).
dc.subjectMonotony
dc.subjectDependence
dc.subjectCorrelation
dc.titleMeasuring monotony in two-dimensional samples
dc.typeJournal Article
dc.rights.accessrightsOpenAccess
dc.identifier.doi10.1080/00207390903477418
dc.identifier.roid13855en_NZ
aut.relation.endpage427
aut.relation.issue3
aut.relation.startpage418
aut.relation.volume41
pubs.elements-id13494


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