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Application of an Ordinal Probit Regression Model in predicting emergency response rates in the Fire Department of New York City

Ivan L. Pitt, PhD


This article introduces the use of an Ordinal Probit Regression Model to predict emergency response rates in the Fire Department of New York City (FDNY) when data are of the count-type variety and ordinal. The main objective of this article is to model the effects of boroughs, emergency incident types, and the volume of emergency incidents (counts) on response rates for the years 2010-2016. The model framework discusses the model selection criteria when the proportional odds assumptions for ordinal models are no longer valid, and a model in which scale effects are allowed to vary among emergency incident types is preferred. This statistical insight can be used by elected officials and city agencies to evaluate FDNY’s emergency response availability, capability, and operational performance in order to improve emergency services in the five boroughs.


ordinal probit regression, NYC FDNY, emergency response rates, EMS

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