A Study on Casualty Profile Using Logistics Regression

Year 2014, Volume: 2 Issue: 1, 3 - 12, 08.01.2014

Sezgin Özcan

Abstract

The environment faced by today’s servicemen is characterized by continual deployments to combat zones, where troops are exposed to the risks of the battlefield. Casualty, whether to combatants or noncombatants, is an unavoidable reality of war. Although the primary goal of combat is to defeat the enemy, keeping casualties down is important as well. Low numbers of injured or killed soldiers not only maintain the ranks of service members, but also have an incredible effect on morale. The purpose of this study is to create a profile of U.S. Army troops killed or injured due to hostile incidents in Afghanistan and Iraq between 2003 and 2011. The analysis of study my help decision makers to see profile that is most vulnerable to casualty. The first part of our study analyzes the descriptive statistical results and the second part contains the results of multivariate analysis of the casualty status of servicemen of the U.S. Army. As a conclusion for our multivariate model, an actual-duty person who is female, married, serving in the reserve forces, serving in a combat troop, between pay grades E1–E3, serving in Iraq, serving the first deployment is the serviceman with most potential to get injured or killed in the U.S. Army.

Keywords

U.S. Army, The Iraq War, The Afghanistan War, Casualty, Hostile Incident, Logistic Regression

References

• Buzzel,E. & Preston, S. (2007). Mortaility of American
• Troops in the Iraq War. Population and Development Review. Chambers, J. M. & Hastie, T. J.(1992). Statistical Models in
• S. Wadsworth & Brooks/Cole. Curtis, J. & Payne, F. (2010). The differential impact of mortality of American troops in the Iraq War: The nonmetropolitan dimension. Demographic Research.
• Fricker, R. D., & Buttrey, S. (2008). Assessing the Effects of Individual Augmentation (IA) on Active Component Navy Enlisted and Officer Retention. Monterey, CA.
• Hosmer, D. W. & Lemeshow, S. (2000). Applied Logistic Regression. Wiley.
• Hosmer, D. W., & Lemeshow, S. (2000). Model ‐Building Strategies and Methods for Logistic Regression. Applied Logistic Regression, Second Edition, 91-142. Menard, S. (2002).
• Applied logistic regression analysis (No. 106). Sage. Nagelkerke, N. J. (1991). A Note On A General Definition Of The Coefficient Of Determination. Biometrica. Paisant, M. A. (2008).
• The effects of Individual Augmentation (IA) on Navy junior officer retention. Naval Postgraduate School Monterey Ca Smith, G., & Campbell, F. (1980). A critique of some ridge regression methods. Journal of the American Statistical Association, 75(369), 74-81.
• Yamaoka, K., Nakagawa, T., & Uno, T. (1978). Application of Akaike's information criterion (AIC) in the evaluation of linear pharmacokinetic equations.Journal of pharmacokinetics and biopharmaceutics, 6(2), 165-175.
• Veall, M. R., & Zimmermann, K. F. (1996). Pseudo‐R2 Measures For Some Common Limited Dependent Variable Models. Journal of Economic Surveys, 10(3), 241-259.
• Venables, W. N. & Ripley, B. (2002). Modern Applied Statistics with S. Springer.
• Wei, Y., Pere, A., Koenker, R., & He, X. (2006). Quantile regression methods for reference growth charts. Statistics in medicine, 25(8), 1369-1382.