"... In PAGE 6: ... We also compare n2SSTA with our implementation of [2] (denoted as linSSTA) by assuming Gaussian variations and linear delay model for both. From Table5 , we see that in predicting = , n2SSTA matches Monte Carlo simulation well with about 5.5% error, while linSSTA has about 11% error.... In PAGE 6: ... This clearly shows that n2SSTA is not only more general, but also more accurate than linSSTA. Note that n2SSTA has a larger error for Gaussian variation sources in Table5 than for uniform or triangle variation sources in Table 4, and this is because n2SSTA needs bigger bounds (10) for Gaussian variations than for uniform or triangle variations. Interestingly, we nd that both approaches pre- dict the 95% yield point well.... ..."

"... In PAGE 6: ...able 10. Summary of Hypothesis Testing....................................................................................64 Table11 .... In PAGE 74: ... The first step of the model examined the question of whether project resources had an impact on support communication levels and development communication levels. The results of this step of the analysis are presented in Table11 . The findings suggest that project resources play a major role in the levels of both support and development communication activity within a project, especially on the total number of messages posted to the forums and on the total number of bug reports and feature requests filed, just as reported in the original analysis.... ..."

"... In PAGE 2: ... For more detailed information about the project environment and the involved people refer to [Egyed-Boehm, 1997] 1996 Projects In 1996, 14 six-member teams set out to do a very similar task of developing components for an integrated library system. Instead of being given a project, these teams selected their topic out of a wide range of (mostly multimedia related) projects (see Table1 ). Each project was conceived by a real customer from the USC Library and was derived out of a need in his/her community.... ..."

"... In PAGE 2: ... In 1996, 14 six-member teams were evaluated (one other team had incomplete data (Boehm- Egyed, 1997)). Instead of being given a project, those teams selected their topic out of a wide range of multimedia related projects (see Table1 ). Each project was conceived by a real customer from the USC Library and was derived out of a need in his/her community.... ..."

"... In PAGE 2: ... In 1996, 14 six-member teams were evaluated (one other team had incomplete data (Boehm- Egyed, 1997)). Instead of being given a project, those teams selected their topic out of a wide range of multimedia related projects (see Table1 ). Each project was conceived by a real customer from the USC Library and was derived out of a need in his/her community.... ..."

"... In PAGE 1: ... Their client- driven, evolving nature made the set of projects more of an observatory than an experimental laboratory [4]. The projects (see Table1 ) focused on different forms of multimedia data like movies, books, manuscripts, pamphlets, pictures, papers, and so on. However, the project goals were diverse because requirements of an archive for technical reports for instance are quite different from those of an archive for medieval manuscripts.... ..."

"... In PAGE 1: ... Their client- driven, evolving nature made the set of projects more of an observatory than an experimental laboratory [4]. The projects (see Table1 ) focused on different forms of multimedia data like movies, books, manuscripts, pamphlets, pictures, papers, and so on. However, the project goals were diverse because requirements of an archive for technical reports for instance are quite different from those of an archive for medieval manuscripts.... ..."

"... In PAGE 6: ...etter as the scale is increased, i.e. as the data becomes smoother. On the final smooth trend curve, resid(t)in Table1 , a crude linear extrapolation estimate, i.e.... In PAGE 6: ...avelet coefficients at higher frequency levels (i.e. lower scales) provided some benefit for estimating variation at less high frequency levels. Table1 sum- marizes what we did, and the results obtained. DRNN is the dynamic recurrent neural network model used.... In PAGE 6: ...sed. The architecture is shown in Fig. 3. The memory order of this network is equivalent to applying a time- lagged vector of the same size as the memory order. Hence the window in Table1 is the equivalent lagged vector length. In Table 1, NMSE is normalized mean squared error, DVS is direction variation symmetry (see above), and DS is directional symmetry, i.... In PAGE 6: ... Hence the window in Table 1 is the equivalent lagged vector length. In Table1 , NMSE is normalized mean squared error, DVS is direction variation symmetry (see above), and DS is directional symmetry, i.e.... In PAGE 7: ...ion of these results can be found in Ref. [4]. For further work involving the DRNN neural network resolution scale. From Table1 , we saw how these windows were of effective length 10, 15, 20, and 25 in terms of inputs to be considered. Fig.... ..."

"... In PAGE 7: ... Of the 17 practices that had completed at least one improvement project, 12 had full audit as best performance and 15 had managed to meet the self set objec- tives in at least one improvement project. Table5 shows the quality of improvement projects for the various types of practice and the topics chosen. The degree to which the Oxford audit score was met and the objectives achieved were similar in the various types of practice and topic.... ..."