The main objectives of this work is to develop a system of equations to test hypotheses and quantify the effect of fuel treatment efforts on wildfire suppression costs and human and resource impacts. More specifically we will answer the following two questions: (a) Do presuppression fuel treatments have the potential to reduce wildfire suppression costs in the treated area? (b) Do presuppression fuel treatments reduce resource impacts, and avoid harm to communities and human populations? Our system of equations would account for many of the quantitative and qualitative variables that influence the costs of suppression and fuel treatments. The regression models will allow us to test multiple hypotheses about what factors influence the cost of suppression and fuels treatment as well their influence on human and resource impacts. Each of the variables included in the system of equations are considered as a hypothesized variable influencing costs. These hypotheses would be tested based on asymptotic t-statistics on each of the variables. The resulting model can also be applied by fire managers to estimate fuel treatment and suppression costs tied to the specifics of their particular fire situation. The linkage to local fire conditions occurs when the manager sets the values of the independent variables at the levels specific to the fuel treatment the manager wants an estimate of the cost for. The expected benefits of our fire suppression cost research is threefold: (a) to determine if there is or is not a statistical relationship between fuel reduction policies and suppression costs and (b) to determine if there is or is not a statistical relationship between fuel reduction policies and property and resource damages from wildfires. If relationship between fuel treatment and suppression costs and property damage is confirmed, then calculate the net suppression costs savings and hence benefits of fuel reduction treatments. Findings for (a) and (b) would help provide justification to Congress that funding additional fuel reduction treatments is a cost effective way to reduce long run suppression costs and wildfire damages; (c) Our multiple regression fuels treatment cost regression model would account for many of the quantitative and qualitative variables that influence the costs of fuel treatment. The resulting model can be applied by fire managers to estimate the cost of fuel treatments tailored to the specifics of their particular application. The tailoring occurs by setting the values of the independent variables at the levels specific to the fuel treatment the manager wants an estimate of the cost for.