ABSTRACT
The coefficient of Variation (CV) measures the closeness of observed values of a phenomenon, and is limited to data sets of univariate random variables. This article introduces a CV extension to multivariate cases, a measure which we shall call the coefficient of covariation (CCV). The CCV comes in the form of a matrix, the diagonal elements of which are the CVs. The off-diagonal elements of the CCV matrix are CV generalisations derived from covariances and means of the two involved variables. CV measures of the closeness of elements of single data set, and the CCV is intended to measure the closeness of data sets.
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How to cite this article
S. M. Seeletse, 2001. The Coefficient of Covariation. Journal of Applied Sciences, 1: 489-493.
DOI: 10.3923/jas.2001.489.493
URL: https://scialert.net/abstract/?doi=jas.2001.489.493
DOI: 10.3923/jas.2001.489.493
URL: https://scialert.net/abstract/?doi=jas.2001.489.493