A major hotel chain keeps a record of the number of mishandled bags per 1,000 customers. In a recent year, the hotel chain had 3.5 mishandled bags per 1,000 customers. Assume that the number of mishandled bags has a Poisson distribution. What is the probability that in the next 1,000 customers, the chain will have exactly 2 mishandled bags?

1 answer

To find the probability of exactly 2 mishandled bags when the average number of mishandled bags per 1,000 customers is 3.5, we can use the Poisson probability formula:

\[ P(X = k) = \frac{\lambda^k e^{-\lambda}}{k!} \]

where:

  • \( k \) is the number of occurrences (in this case, the number of mishandled bags),
  • \( \lambda \) is the average number of occurrences (the mean),
  • \( e \) is the base of the natural logarithm (approximately equal to 2.71828).

Here, we have:

  • \( \lambda = 3.5 \)
  • \( k = 2 \)

Plugging in these values gives:

\[ P(X = 2) = \frac{3.5^2 \cdot e^{-3.5}}{2!} \]

Calculating \( 3.5^2 \):

\[ 3.5^2 = 12.25 \]

Calculating \( 2! \):

\[ 2! = 2 \]

Now, we need to calculate \( e^{-3.5} \):

Calculating \( e^{-3.5} \) using a calculator or exponential function:

\[ e^{-3.5} \approx 0.0301974 \]

Now substituting these values into the Poisson formula:

\[ P(X = 2) = \frac{12.25 \cdot 0.0301974}{2} \]

Calculating the numerator:

\[ 12.25 \cdot 0.0301974 \approx 0.36901845 \]

Now divide by 2:

\[ P(X = 2) \approx \frac{0.36901845}{2} \approx 0.184509225 \]

Thus, the probability that in the next 1,000 customers, the hotel chain will have exactly 2 mishandled bags is approximately:

\[ \boxed{0.1845} \]