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dc.contributor.advisorAlt, Jonathan K.
dc.contributor.authorIntrater, Bradley C.
dc.dateSep-15
dc.date.accessioned2015-11-06T18:22:24Z
dc.date.available2015-11-06T18:22:24Z
dc.date.issued2015-09
dc.identifier.urihttp://hdl.handle.net/10945/47279
dc.descriptionApproved for public release; distribution is unlimiteden_US
dc.description.abstractIn a fiscally constrained environment, Navy Recruiting Command (NRC) must assign its recruiters to maximize the annual number of accessions by each recruiting station. Our thesis built on research in this area and made use of open source socio-economic data from several sources, including the Internal Revenue Service (IRS) and the Federal Bureau of Investigation (FBI). Beginning with a response variable of annual Navy accessions and a set of 71 independent predictor variables populated from ZIP code-level data, we fit and validated six predictive regression models. Models were fit using multiple linear regression (MLR) at the station level and zero-inflated negative binomial (ZINB) regression at the ZIP code level. We identified average number of recruiters, adjusted gross income (AGI) < $25,000, and total veterans as the principal drivers of accession production. We identified AGI > $200,000, unemployment compensation, and total number of universities in a ZIP code as the principal inhibitors to accessions. With out-of-sample data and using 95% prediction intervals, we tested the performance for each of the MLR models and validated them using the five assumptions of linear models. We tested the ZINB models against an out-of-sample subset using Mean Absolute Deviation (MAD) and true negatives, which verify the prediction rate of structural and random zeros. MAD and true negatives demonstrated improvement from previous zero-inflated Poisson models developed in 2011 by Y. K. Pinelis, E. Schmitz, Z. Miller and E. Rebhan, of the Center for Naval Analysis (CNA), in An Analysis of Navy Recruiting Goal Allocation Models.en_US
dc.publisherMonterey, California: Naval Postgraduate Schoolen_US
dc.rightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.en_US
dc.titleUnderstanding the impact of socio-economic factors on Navy accessionsen_US
dc.typeThesisen_US
dc.contributor.secondreaderButtrey, Samuel E.
dc.contributor.departmentOperations Research
dc.contributor.departmentOperations Researchen_US
dc.subject.authorMultiple-linear regressionen_US
dc.subject.authorzero-inflated Poissonen_US
dc.subject.authorzero-inflated negative binomialen_US
dc.subject.authorLogistic Regressionen_US
dc.subject.authorNavy Recruiting Commanden_US
dc.subject.authorvariable selectionen_US
dc.subject.authorsocio-economic dataen_US
dc.description.serviceLieutenant, United States Navyen_US
etd.thesisdegree.nameMaster of Science in Operations Researchen_US
etd.thesisdegree.levelMastersen_US
etd.thesisdegree.disciplineOperations Researchen_US
etd.thesisdegree.grantorNaval Postgraduate Schoolen_US


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