Predicting U.S. Army Reserve unit manning using market demographics
Parker, Nathan L.
Buttrey, Samuel E.
Alt, Jonathan K.
House, Jeffrey B.
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This thesis develops a data-driven, statistical model capable of predicting a U.S. Army Reserve (USAR) unit’s manning level based on the demographics of the unit’s location. This model will aid decision-makers involved in USAR stationing by assessing the ability of a proposed stationing location to support a unit’s manning requirements. USAR units must recruit the majority of their personnel from the population within immediate proximity to the unit. Since the recruiting boundaries of multiple reserve centers often overlap, this thesis first develops an allocation method that ensures the population is not over-counted. This thesis then develops linear regression, classification tree, and logistic regression models to determine the ability of the location to support manning requirements. These models demonstrate that local demographic factors are a key driver in the ability of unit to meet its manning requirements. In particular, the logistic regression model delivers predictive results that allow decision-makers to identify locations with a high probability of meeting unit manning requirements. The recommendation of this thesis is that the USAR implement the logistic regression model.
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