Which of the following distribution methods or models can most effectively represent the actual arrival times of a bus that runs on an hourly schedule?
→ A Normal distribution is appropriate for modeling variables that cluster around a central mean and have natural variability — such as bus arrival times around a scheduled time. Even though the bus is scheduled hourly, real-world factors (traffic, weather, etc.) will cause actual arrival times to vary normally around the scheduled mean.
Why the other options are incorrect:
A: Binomial is for discrete yes/no trials, not continuous time modeling.
B: Exponential models time between events, typically memoryless — not suitable for arrival distributions with a known mean and variance.
D: Poisson models event counts per time interval, not the timing of continuous events like arrival times.
Official References:
CompTIA DataX (DY0-001) Study Guide – Section 1.3:“Normal distributions are appropriate for modeling real-world continuous variables that fluctuate around a central tendency, such as scheduled processes.”
Statistics for Data Science, Chapter 4 – Distributions:“Arrival times of periodic services often approximate a normal distribution when influenced by continuous variation.”
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