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A process is implemented to propagate uncertainties inherent to the Statistical Energy Analysis (SEA) modeling practice to variance in predictions. A Monte Carlo based approach is scripted for the VA-One environment to account for uncertainties in gross parameters of SEA model subsystems. The variance module of the commercial software is used to estimate possible variations in local modal properties. A first-order expansion solution is applied to integrate uncertainties in the power inputs of the system. The impact of each type of source is assessed in computing overall variance in predictions. The process is applied to analysis of in-flight interior cabin noise predictions using a simplified aft fuselage section SEA model.