Kachapova, FKachapov, I2013-06-072013-06-0720102010International Journal of Mathematical Education in Science and Technology, vol.41(3), pp.418 - 4270020-739Xhttps://hdl.handle.net/10292/5429The 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.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).MonotonyDependenceCorrelationJournal ArticleOpenAccess10.1080/0020739090347741813855