The number of cars waiting at a bank for 60 minutes is as follows. Find the probability of 1 car waiting in line. \[ P(1)=[?] \]

To find the probability of 1 car waiting in line, we can use the Poisson distribution, which is often used for counting the number of events happening in a fixed interval of time. If we denote λ (lambda) as the average number of cars waiting, you'd typically calculate it based on the historical data you have for the bank. Once you have λ, you can calculate \( P(1) \) using the formula: \[ P(k; \lambda) = \frac{e^{-\lambda} \lambda^k}{k!} \] For \( P(1) \), substitute \( k = 1 \) and compute \( P(1) = \frac{e^{-\lambda} \lambda^1 }{1!} \). Make sure to gather accurate historical data to determine λ effectively! If you're interested in refining your approach, consider maintaining a record of the arrival rate of cars over various times to calculate a more precise λ. Also, it's beneficial to check for any patterns in times or days that may affect your results—like rush hours versus quiet times at the bank. Keeping this data can enhance your understanding and improve your predictions!