Problems related to alcohol consumption cause not only extra economic expenses, but are an expense to the health of both drinkers and non-drinkers due to the harm directly and indirectly caused by alcohol consumption. Investigating predictors and reasons for alcohol-related problems is of importance, as alcohol-related problems could be prevented by quitting or limiting consumption of alcohol. We were interested in predicting alcohol-related problems using multiple linear regression and regression trees, and then comparing the regressions to the tree. Impaired control, anxiety sensitivity, mother permissiveness, father permissiveness, gender, and age were included as predictors. The data used was comprised of participants (n=835) sampled from students at Arizona State University. A multiple linear regression without interactions, multiple linear regression with two-way interactions and squares, and a regression tree were used and compared. The regression and the tree had similar results. Multiple interactions of variables predicted alcohol-related problems. Overall, the tree was easier to interpret than the regressions, however, the regressions provided specific predicted alcohol-related problems scores, whereas the tree formed large groups and had a predicted alcohol-related problems score for each group. Nevertheless, the tree still predicted alcohol-related problems nearly as well, if not better than the regressions.