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pessimistic time: the maximum possible time required to accomplish an activity (p) or a path (P), assuming everything goes wrong (but excluding major catastrophes). [2]: 512 most likely time: the best estimate of the time required to accomplish an activity (m) or a path (M), assuming everything proceeds as normal. [2]: 512
These values are used to calculate an E value for the estimate and a standard deviation (SD) as L-estimators, where: E = (a + 4m + b) / 6 SD = (b − a) / 6. E is a weighted average which takes into account both the most optimistic and most pessimistic estimates provided. SD measures the variability or uncertainty in the estimate.
Additionally, each task has three time estimates: the optimistic time estimate (O), the most likely or normal time estimate (M), and the pessimistic time estimate (P). The expected time ( T E ) is estimated using the beta probability distribution for the time estimates, using the formula ( O + 4 M + P ) ÷ 6.
[2] [3] The PERT distribution is widely used in risk analysis [4] to represent the uncertainty of the value of some quantity where one is relying on subjective estimates, because the three parameters defining the distribution are intuitive to the estimator. The PERT distribution is featured in most simulation software tools.
The project has two critical paths: activities B and C, or A, D, and F – giving a minimum project time of 7 months with fast tracking. Activity E is sub-critical, and has a float of 1 month. The critical path method ( CPM ), or critical path analysis ( CPA ), is an algorithm for scheduling a set of project activities. [ 1 ]
Program Evaluation and Review Technique (PERT) Putnam model, also known as SLIM; PRICE Systems Founders of Commercial Parametric models that estimates the scope, cost, effort and schedule for software projects. SEER-SEM Parametric Estimation of Effort, Schedule, Cost, Risk. Minimum time and staffing concepts based on Brooks's law
Formal estimation model: The quantification step is based on mechanical processes, e.g., the use of a formula derived from historical data. Combination-based estimation: The quantification step is based on a judgmental and mechanical combination of estimates from different sources. Below are examples of estimation approaches within each category.
The basis of the method is to have, or to find, a set of simultaneous equations involving both the sample data and the unknown model parameters which are to be solved in order to define the estimates of the parameters. [1] Various components of the equations are defined in terms of the set of observed data on which the estimates are to be based.