Measuring monotony in two-dimensional samples
aut.relation.endpage | 427 | |
aut.relation.issue | 3 | |
aut.relation.startpage | 418 | |
aut.relation.volume | 41 | |
dc.contributor.author | Kachapova, F | |
dc.contributor.author | Kachapov, I | |
dc.date.accessioned | 2013-06-07T07:27:42Z | |
dc.date.available | 2013-06-07T07:27:42Z | |
dc.date.copyright | 2010 | |
dc.date.issued | 2010 | |
dc.description.abstract | The 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.identifier.citation | International Journal of Mathematical Education in Science and Technology, vol.41(3), pp.418 - 427 | |
dc.identifier.doi | 10.1080/00207390903477418 | |
dc.identifier.issn | 0020-739X | |
dc.identifier.roid | 13855 | en_NZ |
dc.identifier.uri | https://hdl.handle.net/10292/5429 | |
dc.publisher | Taylor & Francis | |
dc.relation.uri | http://dx.doi.org/10.1080/00207390903477418 | |
dc.rights | Copyright © 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.rights.accessrights | OpenAccess | |
dc.subject | Monotony | |
dc.subject | Dependence | |
dc.subject | Correlation | |
dc.title | Measuring monotony in two-dimensional samples | |
dc.type | Journal Article | |
pubs.elements-id | 13494 | |
pubs.organisational-data | /AUT | |
pubs.organisational-data | /AUT/Design & Creative Technologies | |
pubs.organisational-data | /AUT/Design & Creative Technologies/School of Computing & Mathematical Science |