Secondly we can look directly for heterogenity of variance in the residuals. This is especially useful in multivariate analyses. However, for obvious reasons this should be done sparingly and you should check that the residual variance is reduced without much change in slope. In the face of persistent heterogeneity of variance, you may want to do a weighted regression. Weighted regressions stretch or compress each of the data points as a way of equalizing the residual variances.
Many statistical packages have a weighted regression option.
To test for such an effect after performing a regression, calculate the residuals about the regression line and do an analysis of variance on the residuals using the 'Number of sub-taxa' column from the output as the grouping factor. If a test expected to be significant comes out non-significant in the presence of heterogeneity of variance, it may be useful to explore the data to see if one or a few taxonomic groups are responsible. CAIC The above tests are conducted automatically in CAIC 2.6.x when two columns of data are chosen for analysis. A positive slope when testing the evolutionary assumptions means that large changes are associated with a large values. A negative slope can be corrected by moving down the scale.