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In the s curve shown above the projects cost

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Unformatted text preview: ases A and B), this kind of model is commonly used to provide a cost estimate for a new NASA system. The project manager needs to understand what confidence he/she can have in that estimate. One set of uncertainties concerns whether the input variables (for example, weight) are the proper explanatory variables for cost, and whether a linear or log-linear form is more appropriate. Model misspecification is by no means rare, even for strictly engineering relationships. Another set of model uncertainties that contribute to the uncertainty in the cost estimate concerns the model coefficients that have been estimated from historical data. Even in a well-behaved statistical regression equation, the estimated coefficients could have resulted from chance alone, and therefore cost predictions made with the model have to be stated in probabilistic terms. (Fortunately, the upper and lower bounds on cost for any desired level of confidence can be easily calculated. Presenting this information along with the cost estimate is strongly recommended.) The above uncertainties are present even if the cost model inputs that describe a new system are precisely known in Phase A. This is rarely true; more often, model inputs are subject to considerable guesswork early in the project life cycle. The uncertainty in a model input can be expressed by attributing a probability distribution to it. This applies whether the input is a physical measure such as weight, or a subjective measure such as a "complexity factor." Model input uncertainty can extend even to a grass-roots cost model that might be used in Phases C and D. In that case, the source of uncertainty is the failure to identify and capture the "unknown-unknowns." The model inputs -- the costs estimated by each performing organization -- can then be thought of as variables having various probability distributions. 5.4.2 Modeling Techniques for Handling Uncertainty The effect of model uncertainties is to induce uncertainty in the model's output. Quantifying these uncertainties involves producing an over...
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