Understanding PERT Formula Proof for Project Management

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SUMMARY

The PERT (Program Evaluation and Review Technique) formula for calculating expected time in project management is defined as Expected time = (Pessimistic value + 4 * Most likely value + Optimistic value) / 6. This formula is closely related to the Beta Distribution function, which is used to model uncertainty in project timelines. The choice of parameters in the Beta Distribution, particularly the shape parameters, is based on empirical experience rather than strict mathematical derivation. Additionally, the formula can be connected to Simpson's rule, which provides a method for approximating averages over intervals.

PREREQUISITES
  • Understanding of PERT (Program Evaluation and Review Technique)
  • Familiarity with Beta Distribution in statistics
  • Knowledge of Simpson's rule in numerical integration
  • Basic project management concepts
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  • Research the mathematical foundations of the Beta Distribution function
  • Study the application of Simpson's rule in estimating averages
  • Explore advanced project management techniques using PERT
  • Examine case studies that utilize PERT for project scheduling
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Project managers, statisticians, and anyone involved in project planning and scheduling who seeks to understand the mathematical principles behind the PERT formula and its applications in estimating project timelines.

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a formula used in project management for determining the expected time of an activity states that:

Expected time = (Pessimistic value + 4*most likely value + Optimistic value)/6

I am searching for a proof for this law. I've searched the internet, and found out it's related to the Beta Distribution function, but little details about how the shape parameters of the beta distribution were chosen and why?

Can someone explain to me the outline of the proof at least?

Thanks in advance.​
 
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That is certainly not a "law" but rather an approximation or estimate. Mathematically, it is simply a "weighted average". I presume that the choice of parameters is based on experience in the field, not mathematics.
 
It looks like it might also have something to do with Simpson's rule. Let f(x) be your estimate of the xth percentile for the time (so e.g. f(50%) is your estimate of the median time required). Suppose your "low estimate" is f(25%), your "medium estimate" is f(50%), and your "high estimate" is f(75%). Simpson's rule tells you that the average of f over the interval (25%, 75%) can be approximated by (f(25%) + 4f(50%) + f(75%))/6. I don't know what good that average does you; perhaps it is somehow related to the expected time.
 

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