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Project duration

As explained in Statistics Every Programmer Needs

Project duration is the total time required to complete a project, calculated here from expected times on the critical path.

Project duration is the total time required to complete a project. In a dependency network, it is calculated by summing expected activity times along the longest path, identified as the critical path. section 13.3.1

Estimate and interpret project duration by:

  • Using a forward pass to calculate each activity’s earliest start and finish times. section 13.3.1
  • Summing expected times on the longest path to obtain the project duration. section 13.3.1
  • Combining critical-path variances, assuming independent activity times, to estimate uncertainty around the duration. section 13.4
  • Comparing a due date with the mean duration through a standard deviation and z-score under a normal distribution. section 13.4

Automated reporting tool

The network analysis estimates a 29-week project duration. With a 30-week due date and a 1.41-week standard deviation, the z-score is 0.709. The supplied z-table interpretation gives a 78.52% completion probability; the SciPy example prints 76.09%. section 13.3.1 section 13.4

Figure 13.2 A visual representation of the automated reporting tool project plan, where each activity is represented by its unique identifier. Lines and arrows between activities represent dependencies. The sequence of digits immediately beneath each activity represents, in order, the activity’s earliest start time, expected duration, and earliest finish time. The earliest times are derived by using a forward pass through the network. Based on this analysis, the project timeline is 29 weeks, which is the length of the longest path in the network.
figure

Visualizes activity dependencies, expected durations, earliest times, and the 29-week longest path. section 13.3.1

How Statistics Every Programmer Needs covers it

How deep it goes
Works through forward-pass timing, critical-path summation, variance aggregation, and normal probability estimation.
Examples
Uses an automated reporting tool network, a 29-week duration, a 30-week due date, and SciPy probability code.
What it assumes
Assumes activity dependencies and time estimates, while developing forward-pass and critical-path reasoning.
What you can do after
Evaluate completion likelihoods using critical-path estimates, z-scores, and scipy.stats.norm.cdf.
How it connects
Connects project duration with critical path, forward pass, variance, standard deviation, normal distribution, and z-score.

This explainer was generated by Manning's knowledge graph from the text of Statistics Every Programmer Needs. The section links point to the parts of the book it draws on.