Parametric Estimating

Parametric estimating is an estimating technique that uses a statistical relationship between historical data and other variables, such as square footage in construction and lines of code in software development for calculating an estimate for activity parameters, such as scope, cost, budget, and duration.

Parametric estimating can produce higher levels of accuracy depending upon the accuracy and sophistication of the underlying data.This technique is used for estimates that are quantitatively based such as dollars per square foot or number of installations per day.

It is relatively a simple method, but not every activity or cost can be estimated quantitatively. This method is also referred to as model estimation.


Steps involved in the development of a parametric model

Determining the scope of the parametric model:
It involves defining the applications, physical characteristics, critical components, and cost drivers of the model.

Collecting data to support model development:
It is easy to understand that the quality of a resulting model is no better than the quality of the underlying data. Data related to both scope and cost needs to be identified and collected.

Normalising data to support model development:
Data normalisation involves making adjustments to the base-cost data to account for the differences between the actual basis of the data for each project and a desired standard basis of data to be used for the parametric model.

Data analysis to support model development:
There are a number of techniques for the analysis of data. A typical data analysis consists of performing regression analysis of costs versus selected design parameters to determine the key cost drivers for the model.

Creating the parametric model application:
This step involves establishment of the user interface and the presentation form for the parametric cost model. Also, in this step, various inputs to the cost model are identified with the help of the mathematical and statistical algorithms developed in the data analysis stage.

Testing the parametric model:
An important step in the development of a cost model is testing its validity and accuracy. It measures how well the model can explain variability in data.

Documenting the parametric model:
In the last step, it is very important to document the resulting cost model and parametric estimating applications. Next, a user model needs to be prepared showing the steps involved in preparing an estimate using the cost model and describing clearly the required inputs to the cost model.

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