Date of Award


Document Type


Degree Name

Master of Science in Cost Analysis


Department of Mathematics and Statistics

First Advisor

Edward D. White, PhD


Cost growth remains a concern for cost analysts, program managers, senior DoD decision-makers, Congress, and even the American public. All of these people have a vested interest in the cost of DoD programs and most would like to see those costs decrease; as such, we need additional tools to help combat cost growth. Previous research creates the foundation for the use of a two-step methodology to help predict cost growth, which we follow closely. First, utilizing logistic regression we analyze whether specific program characteristics predict cost growth within the Engineering and Manufacturing Development (EMD) phase for combined RDT&E and procurement budgets. The second step uses this answer (i.e., a positive response) to find cost growth predictor variables. Specifically, we perform a multiple regression analysis and determine the amount of cost growth incurred by these DoD programs. Through these two steps, we seek to unearth any predictive relationships within the data in order to build a predictive cost growth model. The final models predict whether a program will have cost growth and what the potential amount of the cost growth will be for the combined RDT&E and procurement budgets within the EMD phase of acquisition.

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