{"title":"A Scenario-Based Method for Cost Risk Analysis","authors":"P. Garvey","doi":"10.1080/1941658X.2008.10462211","DOIUrl":"https://doi.org/10.1080/1941658X.2008.10462211","url":null,"abstract":"Abstract This article presents an approach for performing an analysis of a program's cost risk. The approach is referred to as the scenario-based method (SBM). This method provides program managers and decision-makers an assessment of the amount of cost reserve needed to protect a program from cost overruns due to risk. The approach can be applied without the use of advanced statistical concepts or Monte Carlo simulations, yet is flexible in that confidence measures for various possible program costs can be derived.","PeriodicalId":390877,"journal":{"name":"Journal of Cost Analysis and Parametrics","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130952624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Derivation of a Cost Factor for Electronic Data Dissemination: A Case Study","authors":"Virginia L. Stouffer","doi":"10.1080/1941658X.2008.10462207","DOIUrl":"https://doi.org/10.1080/1941658X.2008.10462207","url":null,"abstract":"Abstract We describe derivation of a cost factor for data dissemination. Data dissemination is a subcomponent of data. “Data” are described on the third level of an acquisition system's work breakdown structure (WBS). Data costs are infrequently reported in contractor cost data reports (CCDRs), and the costs of data's subcomponents — collection, editing, and dissemination — never appear in CCDRs. Historical data were limited by an industry-wide change in the way data is collected, edited, and disseminated. We estimate a data cost factor from historical data of like programs, account for electronic vs. paper-based data collection, editing, and dissemination and then develop a fraction to capture the dissemination portion alone.","PeriodicalId":390877,"journal":{"name":"Journal of Cost Analysis and Parametrics","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125924645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Errors-in-Variables Regression for CER Development","authors":"Raymond P. Covert","doi":"10.1080/1941658X.2008.10462208","DOIUrl":"https://doi.org/10.1080/1941658X.2008.10462208","url":null,"abstract":"Regression techniques used to statistically derive cost estimating relationships (CERs) have traditionally been limited to curve fitting of vectors of discrete dependent variables (cost) with vectors of discrete independent variables (cost drivers). The independent variables on which CERs are based are typically assumed to be discrete and non-random in nature. That is one of the primary assumptions underlying the classical least-squares linear regression process (“ordinary least squares” or OLS). However, uncertainty in the dependent and independent variables can arise as a result of the data collection and normalization process, and in such cases, considering the independent as well as the dependent variables to be random variables may be a more realistic assumption. Errors-in-variables (EIV) regression techniques can be used to find appropriate CERs under the assumption that there may be errors in either the dependent or independent variables or even when both are random variables. This technique is applicable to any regression problem where there is uncertainty in some or all of the data. This article provides an introduction to the application of EIV regression to CER development. First, it provides a history and description of EIV. Next, it provides insight into the sources of uncertainty in data used to develop CERs. It also offers a description of some suitable EIV regression techniques and demonstrates one of these techniques using an EIV regression example. Finally, the article discusses other potential applications of EIV regression in the costestimating context.","PeriodicalId":390877,"journal":{"name":"Journal of Cost Analysis and Parametrics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131206473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"An Easy Way to Allocate Support Department Costs Using the Reciprocal Method","authors":"Gerald K. Debusk, T. Forsyth","doi":"10.1080/1941658X.2008.10462209","DOIUrl":"https://doi.org/10.1080/1941658X.2008.10462209","url":null,"abstract":"Abstract Support department cost allocation is important to external and internal users of a company's financial information. It is widely recognized that the reciprocal method provides the most meaningful allocation because it fully recognizes services provided by support departments for other support departments. Despite its superiority over other methods, the reciprocal method is rarely applied in practice because of perceived complexities in its application. This article illustrates a simple method of applying the reciprocal method to allocate multiple support department costs using an Excel spreadsheet.","PeriodicalId":390877,"journal":{"name":"Journal of Cost Analysis and Parametrics","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125438271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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