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72 Military Jet Engine Acquisition those engines that meet these criteria and for which development cost information was available. As we noted in Chapter Five, cost information was not available for all the engines listed in Tables 6.2 and 6.3. 2. Simple Derivatives. Any engines that do not fall into the first cate- gory are in this population. These engines are identified in Table 6.3 by a 1 in the Simple Derivative column, provided the cost data were available. Disappointingly, the residual error (the RMSE) remains rather high for the parametric relationships for both the development cost and development schedule, particularly for derivative engines. This high error should not be completely unexpected given that our uncer- tainty regarding the dependent variable is fairly high (see the discus- sion on data verification in Chapter Five). The estimating relation- ships for development cost and schedule are useful only at the con- ceptual stage of engine development and require caution given the range of uncertainty of the estimate. One should not draw conclu- sions based on small differences in predicted values. Furthermore, the relationships should not be used for estimate validation. Table 6.4 shows the regression results and data summary for new engines.4 Figure 6.1 shows the corresponding residual plot for new engines. The residual error is the difference between the actual value and the predicted value. The smaller the residual, the better the model “fit” to that observation. In essence, the points should be normally distributed around zero with no pattern.5 Similar informa- tion is shown in Table 6.5 and Figure 6.2 for the simple derivative engines. ______________ 4RMSE is essentially the sample standard deviation of the forecasted errors without any degrees-of-freedom adjustment. R-squared, the coefficient of determination, de- scribes the percent of variance explained by the estimate equation. Adjusted R- squared takes into account the degrees of freedom that were lost in the analysis. A value of R-squared closer to one suggests that the estimated regression equation fits the sample data very well, whereas a value closer to zero shows a failure of the estimated regression equation to forecast the sample data. The t-statistic (based on the Student t-test) provides information on the level of significance of the calculated coefficient. It is reported in parentheses below each parameter in Table 6.4 and later tables in this chapter. 5The residual plot is useful for diagnosing model misspecification and heteroskedac- ticity and for potentially identifying outlying observations.PDF Image | Military Jet Engine Acquisition Technology Basics and Cost-Estimating Methodology
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