Recent natural disasters have highlighted the need for increased planning for disruptive events. Forecasting damage and time that a system will be inoperable is important for disruption planning. The resilience of critical infrastructure systems, or their ability to recover quickly from a disruption, can mitigate adverse consequences of the disruption. This paper quantifies the resilience of a critical infrastructure sector through the dynamic inoperability input-output model (DIIM). The DIIM, which describes how inoperability propagates through a set of interdependent industry and infrastructure sectors following a disruptive event, includes a resilience parameter that has not yet been adequately assessed. This paper provides a data-driven approach to derive the resilience parameter through regression models. Data may contain different disruption scenarios, and regression models can incorporate these scenarios through the use of categorical or dummy variables. A mixed-effects model offers an alternate approach of accounting for these scenarios, and these models estimate parameters based on the combination of all scenarios (fixed effects) and an individual scenario (random effects). These regression models are illustrated with electric power outage data and a regional disruption that uses the DIIM to model production losses in Oklahoma following an electric power outage.