Abstract
The Spearman correlation test that was developed for use with two random variables has been used incorrectly for analyzing univariate data for serial correlation. This occurs where measurements of the second random variable are incomplete. Data for assessing watershed change, such as for urbanization and deforestation, are commonly incomplete. Although the Spearman coefficient can be applied to detect serial correlation, the probability density function of the bivariate test statistic is not correct for univariate analyses of serial correlation. This research has produced critical values of the test statistic appropriate for use when the Spearman test is applied to serial correlation testing. These will enable more accurate evaluations of the effects of watershed change, as shown by the applications provided in this technical note.
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