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In probability and statistics, the PERT distributions are a family of continuous probability distributions defined by the minimum (a), most likely (b) and maximum (c) values that a variable can take. It is a transformation of the four-parameter beta distribution with an additional assumption that its expected value is
For example, a triangular distribution might be used, depending on the application. In three-point estimation, three figures are produced initially for every distribution that is required, based on prior experience or best-guesses: a = the best-case estimate; m = the most likely estimate; b = the worst-case estimate
It can be understood as representing the time, effort, and resources required to move from one event to another. A PERT activity cannot be performed until the predecessor event has occurred. PERT sub-activity: a PERT activity can be further decomposed into a set of sub-activities. For example, activity A1 can be decomposed into A1.1, A1.2 and A1.3.
It is ubiquitous in nature and statistics due to the central limit theorem: every variable that can be modelled as a sum of many small independent, identically distributed variables with finite mean and variance is approximately normal. The normal-exponential-gamma distribution; The normal-inverse Gaussian distribution
This determines the shortest time possible to complete the project. "Total float" (unused time) can occur within the critical path. For example, if a project is testing a solar panel and task 'B' requires 'sunrise', a scheduling constraint on the testing activity could be that it would not start until the scheduled time for sunrise. This might ...
Proxy-based estimating (PROBE) (from the Personal Software Process) 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.
Example. The F-expression of the positively skewed Gumbel distribution is: F=exp[-exp{-(X-u)/0.78s}], where u is the mode (i.e. the value occurring most frequently) and s is the standard deviation. The Gumbel distribution can be transformed using F'=1-exp[-exp{-(x-u)/0.78s}] . This transformation yields the inverse, mirrored, or complementary ...
These are easily computed, based on the observation that the probability that k observations in the sample will fall in an interval covering p of the range (0 ≤ p ≤ 1) is p k (assuming in this section that draws are with replacement, to simplify computations; if draws are without replacement, this overstates the likelihood, and intervals ...