This document summarizes modeling methods for ground-level ozone concentrations in the contiguous United States. It describes four modeling methods tested: inverse distance weighting (IDW), ordinary kriging, generalized linear models (GLM), and geographically weighted regression (GWR). IDW and kriging account for spatial autocorrelation in the data. GLM and GWR use solar radiation and relative humidity as predictor variables. Kriging and GWR had the lowest errors when validated against new data points, though all models have limitations due to the characteristics and amount of input data. The document emphasizes that statistical models are abstractions of reality and should adhere to principles like parsimony.