Completed Thesis Projects

Regression for Army Recruiting by Sandra Jackson

Category: ThesisStudents
Posted by: ned
Regression for Army Recruiting by Sandra Jackson

Sandra completed her report in May 2016.

In order to maintain military strength, the United States Army is consistently challenged with recruiting new soldiers. Currently the Army evaluates its recruiting capacity by calculating a weighted average of the previous four years of recruiting data. This report provides: (1) a description of the current method of calculating recruiting capacity; (2) an alternative approach for the calculation; and (3) an evaluation process and corresponding results to identify effective recruiting capacity methods. Specifically, the study analyzes the effectiveness of multiple linear regression and Poisson regression models to compute recruiting capacity. Surprisingly, even though essentially all previous literature on recruiting suggests Poisson regression to model recruiting arrival rates, we show strong empirical evidence that multi-linear regression is a better modeling tool than Poisson regression for the recruiting data. On out-of-sample tests involving 32 competing models, the negative log-likelihood for the multi-linear regression models is, on average over all the models, 11% smaller than the corresponding Poisson regression model. On out-of-sample tests involving an additional 20 models, the negative loglikelihood for the multi-linear regression is on average 85% smaller than the corresponding Poisson regression. The statistical models for recruiter rate suggest there is great potential for recruiting capacity because socio-economic factors do not limit the number of recruits. In other words, the results suggest that if the Army wants to increase recruits, one additional recruiter results in an additional 0.89 recruits. Analysis of the explanatory power of different socio-economic factors identifies the population of qualified military aged persons as a key indicator, followed by unemployment rate; however, further study is required to compile and evaluate additional socio-economic factors and their contribution to predicting numbers of recruits or the number of recruits per recruiter.

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