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Past research has shown that predicting the cost growth within DoD systems is an important topic. Total program cost growth and predictors of program cost growth have been studied. Kozlak (2017) studied cost growth at four major reviews: Critical Design Review, First Flight, Development Test and Evaluation End, and Initial Operating Capability. This research attempts to assess cost growth and cost variance at similar points in a program life cycle. In the past the majority of studies have been done identifying programs as either: Acquisition Category (ACAT) I and non-ACAT I programs, or Major Defense Acquisition Programs (MDAP) and non-MDAP programs. This research has data that is able to highlight ACAT II and ACAT III programs. This research also attempts to create a CER for the relationship between Other Government Costs (OGC)-to-contract costs. The research is not attempting to definitively evaluate or confirm the effects of program characteristics, but is rather trying to guide the bolstering of POE databases and POE research. This database and POE research should highlight cost growth and cost variance for ACAT II and ACAT III programs. Such programs are not highlighted in Selected Acquisition Reports (SAR) or the current cost growth literature.

Issues for have thematic title. ; Reports submitted on behalf of various ministries. ; Latest issue consulted: 14th Lok Sabha, 14th rept. (2007/2008). ; Mode of access: Internet.

Testing for independence in two-way contingency tables [ILLEGIBLE][ILLEGIBLE] using a chi-square test that measures the goodness of fit between the observed data and the expected values. This article examines the expected values normally employed in such analyses. It first notes that the usually derived expected values are based on assumptions about the distributional form of an underlying probability density function that may not always be well-founded. Then the article presents an alternative method of estimating expected values, the most possible estimate approach, that does not rely on a priori assumptions about the distributional form. Finally, by establishing that the most possible estimates approximate maximum likelihood estimates, the robustness of the standard maximum likelihood estimator is demonstrated and the researcher is thereby assured of safety in using it without attending to distributional assumptions. The analysis is applied to 3 × 3 tables in this article.