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PERT distribution information


PERT
Probability density function
Example density curves for the PERT probability distribution
Cumulative distribution function
Example cumulative distribution curves for the PERT probability distribution
Parameters (real)
(real)
Support
PDF

where

CDF

(the regularized incomplete beta function) with
Mean
Median

Mode
Variance
Skewness
Excess kurtosis

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

The mean of the distribution is therefore defined as the weighted average of the minimum, most likely and maximum values that the variable may take, with four times the weight applied to the most likely value. This assumption about the mean was first proposed in Clark, 1962[1] for estimating the effect of uncertainty of task durations on the outcome of a project schedule being evaluated using the program evaluation and review technique, hence its name. The mathematics of the distribution resulted from the authors' desire to make the standard deviation equal to about 1/6 of the range.[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.

  1. ^ Clark CE (1962) The PERT model for the distribution of an activity. Operations Research 10, pp. 405406
  2. ^ "PERT distribution". Vose Software. May 2, 2017. Retrieved July 16, 2017.
  3. ^ Continuous Univariate Distributions - 2nd Ed (1995). Johnson K, Kotz S and Balakkrishnan N. (Section 25.4)
  4. ^ Project Management Body of Knowledge: 5th Ed (2013). Project Management Institute Chapter 6

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