DEGROOT & SCHERVISH PROBABILITY AND STATISTICS THIRD EDITION PDF

Even though the model is well constructed, it is important that the input data is correct to get accurate results. Inaccurate data will lead to wrong decisions. This is done by checking every equation and its diverse courses of action. A trial and error method can be used to solve the model that enables us to find good solutions to the problem.

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Based on the probability of project success the method can be utilized to assist on strategic feasibility analysis issues such as contingency provision, "go-no go" decisions and adopting phased or fast track construction.

The method is developed by applying a risk measurement framework to the project economic structure. Using a variable transformation, it transforms the correlated primary variables and the function to the uncorrelated space. Then utilizing the truncated Taylor series expansion of the transformed function and the first four moments of the transformed uncorrelated variables it approximates the first four moments of the derived variable. Using these first four moments and the Pearson family of distributions the uncertainty of the derived variable is quantified as a cumulative distribution function.

The first four moments for the primary variables are evaluated from the Pearson family of distributions using accurate, calibrated and coherent subjective percentile estimates elicited from experts. The correlations between the primary variables are elicited as positive definite correlation matrices. Project duration is estimated by combining the generalized PNET algorithm to the project economic structure.

This permits the evaluation of the multiple paths in the project network. Also, the limiting values of the PNET transitional correlation 0,1 permits the estimation of bounds on all of the derived variables. Project cost and revenue are evaluated in terms of current, total and discounted dollars, thereby emphasizing the economic effects of time, inflation and interest on net present value and internal rate of return.

The analytical method is validated using Monte Carlo simulation. The validations show that the analytical method is a comprehensive and extremely economical alternative to Monte Carlo simulation for economic risk quantification of large engineering projects. In addition, they highlight the ability of the analytical method to go beyond the capabilities of simulation in the treatment of correlation, which are seen to be significant in the application problems.

From these applications a technique to provide contingencies based on the probability of project success and to distribute the contingency to individual work packages is developed.

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