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The Constructive Cost Model (COCOMO) is a procedural software cost estimation model developed by Barry W. Boehm. The model parameters are derived from fitting a regression formula using data from historical projects (63 projects for COCOMO 81 and 163 projects for COCOMO II).
The model then provides as output various resources requirements in cost and time. Some models concentrate only on estimating project costs (often a single monetary value). Little attention has been given to the development of models for estimating the amount of resources needed for the different elements that comprise a project. [1]
Parametric estimating models are mathematical models containing cost estimating relationships (CERs) developed through data collection and regression analysis. The TruePlanning Software model provides activity-based parametric models to aid in the estimation of new software development, integrations of Commercial Off-the-Shelf (COTS) Software ...
The profit model is the linear, deterministic algebraic model used implicitly by most cost accountants. Starting with, profit equals sales minus costs, it provides a structure for modeling cost elements such as materials, losses, multi-products, learning, depreciation etc. It provides a mutable conceptual base for spreadsheet modelers.
A cost estimate is often used to establish a budget as the cost constraint for a project or operation. In project management, project cost management is a major functional division. Cost estimating is one of three activities performed in project cost management. [3] In cost engineering, cost estimation is a basic activity. A cost engineering ...
The Cigar Box Method is a toolkit which consists of a series of spreadsheets to help entrepreneurs, notably those in agribusiness in emerging markets, to calculate the costs of goods, margins, contribution, break-even quantity and profitability. It can be used for a single product or a complete portfolio of products.
In the context of nonlinear system identification Jin et al. [9] describe grey-box modeling by assuming a model structure a priori and then estimating the model parameters. Parameter estimation is relatively easy if the model form is known but this is rarely the case.
The reduced form however can be identified easily. Fisher points out that this problem is fundamental to the model, and not a matter of statistical estimation: It is important to note that the problem is not one of the appropriateness of a particular estimation technique.