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Parametric cost estimating is best defined as a cost estimating technique that is mathematically driven. This approach relies on statistical relationships between historical data and other variables to develop estimates for costs. By using mathematical models and formulas, parametric estimating allows for the analysis of various parameters that can influence costs, resulting in estimates that can be more accurate and reliable than those generated by methods that rely solely on historical data or simpler calculations.

This technique involves identifying key cost drivers and using them to create a formula or algorithm that can predict costs based on the size, scope, or complexity of a project. The strength of parametric cost estimating lies in its ability to quickly provide estimates for various scenarios based on quantifiable data, making it particularly useful in project management and budgeting.

In contrast, methods that focus only on historical data may not account for changes in project scope or conditions, while the assertion that a technique guarantees precise cost estimations is misleading, as all estimating methods have inherent uncertainties. Additionally, suggesting that a basic approach exists without calibration requirements overlooks the analytical depth involved in parametric estimating, which often requires adjustments and calibrations based on project specifics to ensure accuracy.