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Presuming that this is legitimate is dubious, but it leads to a mean of 3 7 6 4 =4 5 associated with a probability distribution that looks Normal, and mean and variance estimates look objectively determined in the sense that the observation of 15 can be rejected as an outlier relative to the normal variability re ected by the other four observations using classical statistical tests In a project planning context, some seasoned users of PERT (Moder and Philips, 1970) might prefer to start by assuming a standard Beta distribution approximation Then the mean value is approximated by b 4m h 6 where b is a plausible pessimistic value, m the most likely value, and h is a plausible optimistic value Table 15.

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For example, imagine a software development project that is designed to bring the organization s current accounting system into compliance with new nancial legislation A sample business requirement would be Extend the application to support the new legislation A sample user requirement might be Allow authorized users to produce reports that identify the time, date, reason, and authorization for any nancial data changes A sample system requirement is The new application code should be built in Java, connect to the existing MySQL database, and run on the existing server A sample deployment requirement is Establish a process for automatically deploying the modi ed application to a staging environment for testing, then automatically deploying the application on the mainframe A sample upgrade requirement is The upgrade must preserve all existing data and not require the system to be of ine during business hours.

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5 raises the obvious problem what is the most likely value If the value 6 is assumed, b is estimated by 3 and h by 15, and the estimate of mean duration is 3 4 6 15 =6 7 The corresponding standard deviation estimate is h b =6 15 3 =6 2 This Beta distribution approximation accommodates the obvious skew in the data and makes use of all the data It is somewhat rough and ready and does not encourage examination of the pessimistic tail, but it is clearly more robust than a Normal distribution approach with or without outlier rejection Some people who are particularly partial to a Bayesian approach (eg, Waters, 2001) may argue that the corporate standard time for approval of 3 weeks is a form of prior distribution with zero variance and the data of Table 15.

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If the last inequality is indeterminate, the conclusion of the test is also indeterminate Anderson (1996) shows that these direct nonparametric tests avoid the use of generalized Lorenz curves and belong to the family of Pearson goodnessof- t tests His nonparametric tests start with the multinomial allocation of units to k class intervals He assumes independence and that the cell sizes satisfy Tpi > 5 The test statistic is a quadratic form in normal variables leading to a c2 with k - 1 degrees of freedom Since our application to portfolio return data does not satisfy his (independence) assumptions, we seek an extension of his tests We do not recommend his c2 goodness-of- t test Deshpande and Singh (1985) and Schmid and Trede (1998) take a different approach based on Kolmogorov-type test statistics They use the one-sample problem whereby it is assumed that one of two CDFs is completely known..

5 de ne posterior information: the two need to be combined The key effect of this perspective is to bring the mean of 7 (or 5) closer to 3 and tighten the distribution, whatever assumptions are made for the posterior distribution Some people with a classical statistical training whether or not they are Bayesians might treat the 3 7 6 4 =4 5 estimate of the mean and an associated variance as Normally distributed uncertainty related to normal variability , which occurs approximately four times out of ve, while associating the 15 week observation with abnormal variability , which occurs approximately one time out of ve This perspective would recognize that normal variability may be important, but that abnormal variability may be much more important and may need to be understood, incorporated in estimates, and managed, if it is costeffective to do so.

Once gathered and categorized, all requirements should be uniquely labeled, and recorded in the SRS document This document is used later for prioritizing requirements and for entering them into the requirements management system..

This immediately raises the need to model abnormal variability in subjective terms, if it matters, in a manner that facilitates its management We.

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