In this paper, we contrast three regression methods on the example of finding the correlates of child mortality rates (under5mort) by province in Indonesia. Factors examined include the average high-school enrollment rate (enrollment), average level of poverty (poverty), and the total fertility rate (TFR). The three methods we compared are: 1) traditional multiple linear regression (MLR), 2) eXtreme Gradient Boosting (XGBoost) algorithm, and 3) Random Forest (RF) algorithm. We find that TFR and poverty show statistically significant relationship with the mortality rates of children under age 5, while the high-school enrollment rate does not. Results are qualitatively same for all three methods but differ in the value of coefficient of determination R-squared. XGBoost has highest coefficient of determination and is thus the best fitting model for our data samples. Based on order of features, the most important among the three factors is TFR and the next is poverty level. We propose that XGBoost and RF regression methods could be successfully applied to other demographic analyses in which the relationship between the feature variables and the predictor is not linear. © 2019 IEEE.