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Statistical Analysis 75 It is interesting to note that for the new engine developments, basic performance measures define the CER. There was no significant technical maturity/risk measure that correlated with development cost and development schedule. For the simple derivative engines, by comparison, a term dependent on the number of full-scale test hours appears to be correlated. While this term was categorized as “programmatic,” it is also partially related to technical risk and ma- turity. One would expect that a more complex, technically challeng- ing derivative engine would require more test hours than a simpler one. DEVELOPMENT TIME The parametric relationship for aircraft engine development time is shown in Table 6.6 with summary information on statistics. Figure 6.3 shows the corresponding residual plot for new engines. The data included in Tables 6.2 and 6.3 for the development time analysis are for only those engines listed in Table 5.3 in Chapter Five. While the t- statistics indicate that both parameters (2.876 and 3.581) are signifi- cant, the regression statistics (R-squared and RMSE) are very poor. Table 6.6 Development Time Regression Results Variable lndtimem neweng lnopr Number of Obser- vations 33 33 33 Mean 3.473 0.515 3.038 Median 3.527 1.000 3.063 Standard Deviation 0.510 0.508 0.233 Minimum 2.298 0.000 2.565 Maximum 4.357 1.000 3.440 lndtimem = –0.243 + 0.425 neweng + 1.15 lnopr (2.88) (3.58) R-squared = 0.3741. Adjusted R-squared = 0.3324. RMSE = 0.4169. lndtimem = natural log of the development time in months (from contract award to low-rate production release; for older engines, the finish date corresponds to the MQT date). neweng = a binary variable (1 or 0). It is true (1) when the engine is the first production engine of a family, incorporates advanced technology, or is a new centerline design. Otherwise, the variable is false (0). lnopr = natural log of the overall pressure ratio.PDF Image | Military Jet Engine Acquisition Technology Basics and Cost-Estimating Methodology
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