This article reviews econometric methods for health outcomes and health care costs that are used for prediction and forecasting, risk adjustment, resource allocation, technology assessment, and policy evaluation. It focuses on the principles and practical application of data visualization and statistical graphics and how these can enhance applied econometric analysis. Particular attention is devoted to methods for skewed and heavy-tailed distributions. Practical examples show how these methods can be applied to data on individual healthcare costs and health outcomes. Topics include: an introduction to data visualization; data description and regression; generalized linear models; flexible parametric models; semiparametric models; and an application to biomarkers.