Which cost estimating technique uses statistical relationships between variables for quick estimates?

Which cost estimating technique uses statistical relationships between variables for quick estimates?

• A. A technique that uses detailed engineering data to estimate costs.
• B. A technique used for quick estimates based on statistical relationships between variables.
• C. A technique that uses historical data from similar projects to estimate costs.
• D. The real costs incurred for a project.

Answer: (B)

Parametric estimating uses statistical relationships between measurable variables (cost drivers) and total project cost. By deriving a cost estimating relationship from historical data—such as cost per square foot, per unit of capacity, or per mile of road—you can generate quick estimates once the project size or scope is defined. This approach is faster than itemizing every component in detail, because you apply a unit cost or a regression equation to the known parameter rather than building up costs piece by piece. It’s especially useful in early planning or when time is limited and the project parameters are well defined. However, accuracy depends on the quality and relevance of historical data and the assumption that future costs will behave like past costs, which can be affected by design changes or new technologies.

Parametric estimating uses statistical relationships between measurable variables (cost drivers) and total project cost. By deriving a cost estimating relationship from historical data—such as cost per square foot, per unit of capacity, or per mile of road—you can generate quick estimates once the project size or scope is defined. This approach is faster than itemizing every component in detail, because you apply a unit cost or a regression equation to the known parameter rather than building up costs piece by piece. It’s especially useful in early planning or when time is limited and the project parameters are well defined. However, accuracy depends on the quality and relevance of historical data and the assumption that future costs will behave like past costs, which can be affected by design changes or new technologies.