Answer:
Let X the random variable who represent the number of occurences in a period of time for the calls.
For this case we have the following parameter [tex] \lambda = 1 \frac{call}{2 minutes}[/tex]
And we are interested in the expected number of calls in one hour.
We know that 1 hr = 60 mins so then the expected number of calls that arrive in one hour are:
[tex] \lambda = \frac{1 call}{2 minutes} * \frac{60 minutes}{1 hour} = 30 calls per hour[/tex]
Step-by-step explanation:
Definitions and concepts
The Poisson process is useful when we want to analyze the probability of ocurrence of an event in a time specified. The probability distribution for a random variable X following the Poisson distribution is given by:
[tex]P(X=x) =\lambda^x \frac{e^{-\lambda}}{x!}[/tex]
And the parameter [tex]\lambda[/tex] represent the average ocurrence rate per unit of time.
Solution to the problem
Let X the random variable who represent the number of occurences in a period of time for the calls.
For this case we have the following parameter [tex] \lambda = 1 \frac{call}{2 minutes}[/tex]
And we are interested in the expected number of calls in one hour.
We know that 1 hr = 60 mins so then the expected number of calls that arrive in one hour are:
[tex] \lambda = \frac{1 call}{2 minutes} * \frac{60 minutes}{1 hour} = 30 calls per hour[/tex]