Although regression adjustment can provide a useful alternative to direct adjustment, especially when data are sparse, many researchers are unaware that adjusted summary measures can be easily derived from regression coefficients. In a non-technical discussion with examples, the direct adjustment procedure is compared with three methods of regression adjustment based on analysis of covariance models: the conditional prediction method, the stratified prediction method, and the marginal prediction method. Both the stratified prediction and direct adjustment methods yield summary measures that are weighted averages of stratum-specific measures, while adjusted measures from the conditional prediction method are similar to stratum-specific estimates. In contrast to the other adjustment procedures, which can use internal or external weights, the marginal prediction method always gives an internally adjusted measure. Under certain conditions, the three regression adjustment procedures produce identical results. Major advantages of direct adjustment include computational simplicity and relatively few statistical assumptions. Regression adjustment, however, is more convenient for statistical tests for interactions and group differences, and often precludes the need to categorize continuous variables, so that problems with empty strata are avoided.