Inference for Under-Dispersed Data: Assessing the Performance of an Airborne Spacing Algorithm

Poisson regression is a commonly used tool for analyzing rate data; however, the assumption that the mean and variance of a process are equal rarely holds true in practice. When this assumption is violated, a quasi-Poisson distribution can be used to account for the existing over- or under-dispersion. This paper presents an analysis of a study conducted by NASA to assess the performance of a new airborne spacing algorithm. A deterministic computer simulation was conducted to examine the algorithm in various conditions designed to simulate real-life scenarios, and two measures of algorithm performance were modeled using both continuous and categorical factors. Due to the presence of under-dispersion, tests for significance of main effects and two-factor interactions required bias adjustment. This paper presents a comparison of tests of effects for the Poisson and quasi-Poisson models, details of fitting these models using common statistical software packages, and calculation of dispersion tests.