The video computes the total time to complete both reports by performing an elementwise addition of the two simulated completion-time arrays. The total time is simply their summation, represented in code as `duration = A+B`.
Conditions: A and B must be arrays of the same length.; Addition is performed entrywise across matched simulation runs.
The video computes the total time to complete both reports by performing an elementwise addition of the two simulated completion-time arrays. The total time is simply their summation, represented in code as `duration = A+B`.
Conditions: A and B must be arrays of the same length.; Addition is performed entrywise across matched simulation runs.
The two report completion-time arrays are generated using the `np.random.uniform` function. Report A is modeled by drawing `sims` independent values from a uniform distribution on the interval from 1 to 5 hours, and Report B is modeled by drawing `sims` independent values from a uniform distribution on the interval from 2 to 6 hours.
Conditions: Use the uniform model introduced earlier.; `sims` must already be defined.
The two report completion-time arrays are generated using the `np.random.uniform` function. Report A is modeled by drawing `sims` independent values from a uniform distribution on the interval from 1 to 5 hours, and Report B is modeled by drawing `sims` independent values from a uniform distribution on the interval from 2 to 6 hours.
Conditions: Use the uniform model introduced earlier.; `sims` must already be defined.
Monte Carlo simulation needs thousands or millions of repeated random draws because a single random pick is not enough. Repeating the random selection many times and storing all the selected values allows us to use those stored results to approximately estimate the distribution around the time to complete both reports.
Conditions: The random selection process is repeated many times.; The selected values are stored.
Monte Carlo simulation needs thousands or millions of repeated random draws because a single random pick is not enough. Repeating the random selection many times and storing all the selected values allows us to use those stored results to approximately estimate the distribution around the time to complete both reports.
Conditions: The random selection process is repeated many times.; The selected values are stored.
A red vertical line is placed at 9 on the histogram to mark the 9-hour barbecue deadline threshold. This makes it easier to visually identify the region corresponding to the event where the total time to complete both reports exceeds the deadline.
Conditions: The barbecue is in 9 hours.; The line marks the threshold on the horizontal axis.
A red vertical line is placed at 9 on the histogram to mark the 9-hour barbecue deadline threshold. This makes it easier to visually identify the region corresponding to the event where the total time to complete both reports exceeds the deadline.
Conditions: The barbecue is in 9 hours.; The line marks the threshold on the horizontal axis.