Using Bivariate Correlations, you produced a correlation matrix for Sales in thousands by Fuel efficiency and, surprisingly, found a negative correlation. Upon removing an outlier and using Log-transformed sales, the correlation became positive, although not significantly different from 0. However, you found that by computing the correlations separately for trucks and autos, there is a positive and statistically significant correlation between sales and fuel efficiency for automobiles.

Furthermore, you found similar results without the transformation using Spearman's rho, and perhaps are wondering why you should go through the effort of transforming variables when Spearman's rho is so convenient. The measures of rank order are handy for discovering whether there is any kind of association between two variables, but when they find an association it's a good idea to find a transformation that makes the relationship linear. This is because there are more predictive models available for linear relationships, and the linear models are generally easier to implement and interpret.