NotesFAQContact Us
Search Tips
Peer reviewed Peer reviewed
Direct linkDirect link
ERIC Number: EJ884053
Record Type: Journal
Publication Date: 2010
Pages: 10
Abstractor: As Provided
Reference Count: 8
ISSN: ISSN-0020-739X
Measuring Monotony in Two-Dimensional Samples
Kachapova, Farida; Kachapov, Ilias
International Journal of Mathematical Education in Science and Technology, v41 n3 p418-427 2010
This note 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 two-dimensional 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. This article 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. (Contains 1 table and 1 figure.)
Taylor & Francis, Ltd. 325 Chestnut Street Suite 800, Philadelphia, PA 19106. Tel: 800-354-1420; Fax: 215-625-2940; Web site:
Publication Type: Journal Articles; Reports - Descriptive
Education Level: Higher Education
Audience: Teachers
Language: English
Sponsor: N/A
Authoring Institution: N/A