# An introduction to parametric estimating

As mentioned previously, one of the key indicators of how well a regression equation explains the data is the R 2 value. The models highlight the design parameters used, and can provide key statistical relationships and metrics for comparison with other projects.

In simple terms, it is one measure of how well the equation explains the variability of the data. Once you have performed the regression analysis, and obtained an algorithm with a reasonably high R 2 value, you still need to examine the algorithm to ensure that it makes common sense.

Regression analysis tends to be a continuing trialand-error process until the proper results are obtained that appears to explain the data. Therefore, this technique does not provide a very reliable estimation.

This information is them implemented for the purposes of calculating and demonstrating an estimate for the entity of activity parameters. The algorithms will usually take one of the following forms: An underlying assumption of parametric estimating is that the historical framework on which the parametric model is based is applicable to the new project i.

Considers the worst case and assumes that almost everything goes wrong. Sometimes, you will find that erratic or outlying data points will need to be removed from the input data in order to avoid distortions in the results.

In Table 2, the design parameters are displayed as used in the model raised to a power where needed and shown against the actual costs and the predicted costs from the estimating algorithm. The model should generate current year costs or have the ability to escalate to current year costs.

Key design parameters that appear to affect the costs of cooling towers are the cooling range, approach, and flow rate. If two estimators input the same values for parameters, they will get the same resulting cost. After all of the individual algorithms have been developed and assembled into a complete parametric cost model, it is important to test the model as a whole against new data data not used in the development of the model.

Parametric cost estimating models are useful tools for preparing early conceptual estimates when there is little technical detail to provide the basis to support using more detailed estimating methods.

If many of the estimates are padded, the project will have an extravagant schedule. Capacity factor and equipment factors estimates are simple examples of parametric estimates; however sophisticated parametric models typically involve several independent variables or cost drivers.

Increasing the approach will result in a less costly cooling tower as it increases the efficiency of the heat transfer taking place. Some examples of these variables include square footage in a contraction project, the number of lines or code that exist in a software application, and other similar variables.

In this case, there is no need of creating a schedule or a budget. These appear to be reasonable assumptions. This technique can be used with limited information available about the project.

In this technique, the cost of each single activity is determined with the greatest level of detail at the bottom level and then rolls up to calculate the total project cost.

Normalizing for cost scope implies making adjustments i. A project manager plays a key role during the process of estimation.

A user manual should be prepared showing the steps involved in preparing an estimate using the cost model, and describing clearly the required inputs to the cost model. The level at which the cost data is collected will affect the level at which the model can generate costs, and may affect the derivation of the CER s.

Typically, data normalization implies making adjustments for escalation, location, cost scope, site conditions, and system specifications. It is usually desirable to make available the actual regression datasets, and the resulting regression equations and test results.

It is the fastest technique to estimate cost but least accurate. Location may also be important to support cost normalization. Most Likely Cost Cm: The model should be based on actual costs from complete projects, and reflect your organization s engineering practices and technology.

Collecting data to support model development Data collection and development for a parametric estimating model requires a significant effort. Three-Point Estimating This technique is used to reduce the biases and uncertainties in estimating assumptions. In other words, perform a cursory examination of the model to look for the obvious relationships that you expect to see.

If the relationships from the model appear to be reasonable, then you can run additional tests for statistical significance t-test and F-testand to verify that the model is providing results within an acceptable range of error.Let's take a look at some examples to help explain parametric estimating a bit further.

Let's say you are in charge of administering a test to 5 students at the local college. The test will consist of 50 multiple choice questions and each student will take it individually. Parametric Estimating - Multiple Regression 0 stars out of 5 based on 0 user ratings.

This job aid is intended as a complement to the Linear Regression job aid which outlines the process of developing a cost estimating relationship (CER), addresses some of the common goodness of fit statistics, and provides an introduction to some of the issues.

CHAPTER 2 PARAMETRIC ESTIMATING INTRODUCTION In this part of the text the concept of parametric estimating will be introduced, defined and illustrated. Additionally, the steps involved in the successful development of a parametric cost-estimating model will be identified and discussed individually.

Parametric Estimating_Nonlinear Regression 0 stars out of 5 based on 0 user ratings. This job aid is intended as a complement to the Linear Regression job aid which outlines the process of developing a cost estimating relationship (CER), addresses some of the common goodness of fit statistics, and provides an introduction to some of the issues.

An Introduction to Parametric Estimating Mr. Larry R. Dysert, CCC ACE International describes cost estimating as the “predictive process used to quantify, cost, and price the resources required by the scope of an asset investment option, activity, or.

PARAMETRIC ESTIMATING A parametric estimating model is a mathematical representation of cost relationships that provide a logical and predictable correlation between the physical or functional characteristics of a project (plant, process system, etc.) and its resultant cost [4].

An introduction to parametric estimating
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