Spatial distribution, integration and determinants of family planning service provision in Lubumbashi, DRC: a cross-sectional analysis from a health facility census. [PDF]
Mosuse MA +14 more
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Changes in Oncology Care Provision Following Safety-Net Hospital Acquisition by Health Systems. [PDF]
Bromley-Dulfano RA +3 more
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Organizational and community determinants of hospital-based violence prevention programs in the United States. [PDF]
Karabukayeva A +3 more
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Nutraceutical potential of prekese (<i>Tetrapleura tetraptera</i>) fruit extract: synergizing growth performance, oxidative stability, and economic viability in Nile tilapia (<i>Oreochromis niloticus</i>). [PDF]
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Dealing with Fuzziness in Cost-Volume-Profit Analysis
Accounting and Business Research, 1990Abstract A major limitation of the traditional cost-volume-profit (CVP) analysis is its inability to account for uncertainty and risk. This deficiency has been resolved by the development of probabilistic and stochastic CVP models which require precise numerical assessment of uncertainties in sales demand.
Y. Lilian Chan, Yufei Yuan
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Cost-Volume-Profit Analysis Adjusted for Learning
Management Science, 1977A model is developed for cost-volume-profit analysis which incorporates a nonlinear cost function to express the effects of employee learning. Sensitivity analysis is applied to the model to assess the impact of estimation errors in the learning rate and steady-state production time on estimated profit and break-even quantities.
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Incremental cost-volume-profit analysis
Journal of Accounting Education, 1990Abstract The discussion of sensitivity analysis for cost-volume-profit model is limited in most cost accounting textbooks. In this article, incremental analysis, a methodology that involves a model of relative incremental changes, is introduced as a framework for conducting sensitivity analysis.
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A STOCHASTIC COST VOLUME PROFIT ANALYSIS
Decision Sciences, 1973ABSTRACTIn most models dealing with the cost‐volume‐profit analysis, the costs and price are usually assumed to be deterministic variables. In this paper, we build such models by explicitly assuming that they are stochastic variables. We then evaluate a number of statistical properties of these models and, in turn, consider the usefulness of the models
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