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Estimations by analogy are effective in low-risk projects and projects for which similar historical data exists. Unfortunately, they cannot be used in innovative projects unless a functioning prototype already exists, and even then, the estimate is just a good guess. If used for innovative projects, estimates should be corroborated by one of the other approaches. 5.10.3 Estimation by Using Parametric Models Parametric models are mathematical representations by formulas, which use effort-qualifying variables representing either quantitative or qualitative attributes of software development. Quantitative parameters describe, for example, size, number of defects, number of violated coding rules, or number of months. Qualitative parameters can describe, for example, software complexity, team cohesion, organization maturity, or a level of process automation. Historically, parametric approaches have their roots in Constructive Cost Model (COCOMO), which was originally developed in the early 1980s by Barry Boehm [6]. While superseded by more modern approaches, including COCOMO II [7,8], the original COCOMO was the rst attempt at a comprehensive, parametric model to estimate software development effort. The following sidebar describes its basic features.

Students can t always pick and choose the jobs that they would like to do. Of course you may have gained relevant work experience, but the reality is that part-time jobs are more often about paying bills. Don t just list the jobs that you had explain why you took them too. Consider these examples: I worked in the students union bar because I needed some way to avoid getting into too much debt during my course. In the summer holidays, I went home and took temping jobs in various local companies so that I could avoid having to take out any further student loans. I managed to get a job as a runner during the summer for the local radio station. It was unpaid, but I was glad to do it because I thought that getting some practical experience was more important than earning a couple of quid an hour working in a pub or an office.

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In the above equation ki = 2k2 k1 is the free-space wave number for the IM product. Comparing (13.22) and (13.23) we obtain ki sin ki sin

His intention was to give de nition to a body of studies which paid the same attention to the most ordinary everyday activities as generally accorded to extraordinary ones, on the assumption that practices of explanation, and explanations themselves, are incarnated in each other: the activities whereby members produce and manage settings of organized everyday affairs are identical with member s procedures for making those settings accountable (Gar nkel, 1967: 1) Gar nkel has developed a new discipline.

family. I let go of the narrower Radish purpose and embraced a broader intention. It was time for me to move on from Radish, so that I could, if it was not too late, have a real baby. I somehow felt better, as if there was a divine purpose in my leaving the company. So on that plane ride, I wrote my resignation letter and committed to the most difficult decision in my life. The words f lowed onto the paper as did my sorrow. I am writing to let you know of my decision to leave Radish. I can no longer contribute to my fullest in this environment. I cannot live true to my own highest convictions. . . . In arriving home that night to Dick s comforting embrace, I knew I made the right decision. With a broken heart and lump in my throat, I came in the next day to resign from my board and management positions. What a sad day! I did what I had to do. Like the mother in the biblical story of King Solomon who gave her baby to the false mother to save the child s life, I gave my first baby, Radish, to stepparents who I knew would keep her alive. Dick remained. A few days later at my going-away party, cold rain and sleet incessantly fell outside. It was as if the entire world was crying with me. I sang this song: Take good care of my baby. Tell her that I love her so. And if you really love her, Then help her grow and prosper. Baby . . .

guard bits, 24 inaccuracies, 42 in EI, 180 intermediate calculation, 28 machine epsilon, 24 mantissa, 24 misconceptions, 28 normalized number, 24 over ow, 23 precision, 24 25 reproducibility, 28 29 rounding, 24, 93, 94 rounding error, 17 truncation, 24 under ow, 23, 25 FORTRAN, xv, 50, 51, 92, 265 function maximization, see optimization algorithm Gauss, xv, 7, 9, 66 68, 74, 153, 155, 156, 176, 181, 182, 187, 188, 199, 213, 258 Gauss Markov, 79 80 Gauss Newton method, see optimization algorithm, Gauss Newton method generalized Cholesky, 147, 151, 155 159 numerical examples, 158 159 generalized inverse, 147, 152, 151 155 minimum norm, 153 Moore Penrose, 151 normalized, 153 numerical examples, 154 155 re exive, 153 weak, 153 generalized linear model, 82 interactions, 82 link function, 82 genetic algorithm (GA), see optimization algorithm, genetic algorithm geographic information systems (GIS), 219, 266 Gerschgorin bounds, 158 Gerschgorin circle theorem, 158 Gibbs sampler, 6, 119, 128, 139, 232 full conditional distributions, 129

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