The purpose of this research is to determine an appropriate method for estimating cargo airdrop collateral damage risk. Specifically, this thesis answers the question: How can mission planners accurately predict airdrop collateral damage risk? The question is answered through a literature review and a thorough examination of a data set of real world airdrop scoring data. The data were examined to determine critical factors that affect airdrop error risks as well as to determine the characteristics of airdrop error patterns. Through this research it was determined that bivariate normal distributions with parameters pairs determined by empirical data are appropriate for modeling cargo airdrop errors patterns. Collateral risk is estimated by summing numerical integrations of a fit bivariate normal distribution for each drop type across rectangular representations of drop field objects in the field of concern. Airdrop altitude and chute type are found to make a statistically significant difference in airdrop error patterns while airdrop aircraft type does not appear to have a significant effect. This research methodology is implemented in an EXCEL spreadsheet tool that can be easily used by airdrop mission planners including an extension, requested by the research sponsors, to handle bundled drops that fall in a linear spread
