Binomial probability or poisson?

In summary, the conversation discusses finding the maximum number of reservations an airline should book in order to have a probability of no more than 5% of not having enough seats for booked passengers. This is an example of a binomial distribution, where the no show rate of passengers is 9%, or 0.09. The conversation also includes a condition that needs to be satisfied in order to find the maximum value of reservations.
  • #1
Tbx013
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This the only question I'm having issues with. It may be a binomial distribution or poissm, not really sure.

If an airplane has 224 seats and the no show rate of passengers with reservations is .09 how many reservations should the airline book such that the probability of not enough seats for booked passengers showed up is AT MOST 5%.
 
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  • #2
Tbx013 said:
This the only question I'm having issues with. It may be a binomial distribution or poissm, not really sure.

If an airplane has 224 seats and the no show rate of passengers with reservations is .09 how many reservations should the airline book such that the probability of not enough seats for booked passengers showed up is AT MOST 5%.

Wellcome on MHB Tbx013!...

I think that this is a typical example of a binomial distribution. What you have to do is find an oversupply of seats m so that the probability of having a passenger that no place is no more than .05. In practice you have to find the maximum value of m for which the following condition is satisfied ...

$\displaystyle \sum_{k=1}^{m} \binom {224+ k}{224 + m} p^{224+ k}\ (1-p)^{224 + m - k} < .05,\ p= 1-.09= .91$

Kind regards

$\chi$ $\sigma$
 
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FAQ: Binomial probability or poisson?

What is the difference between binomial probability and Poisson distribution?

The main difference between binomial probability and Poisson distribution is that binomial probability deals with discrete events with a fixed number of trials, while Poisson distribution deals with continuous events with a variable number of trials. In binomial probability, the probability of success remains constant, while in Poisson distribution, the probability of success changes with each trial.

How do you calculate the probability of success in a binomial experiment?

The probability of success in a binomial experiment can be calculated by dividing the number of successes by the total number of trials. For example, if there are 10 trials and 3 successes, the probability of success would be 3/10 or 0.3.

What is the formula for calculating the probability of a specific number of events occurring in a Poisson distribution?

The formula for calculating the probability of a specific number of events occurring in a Poisson distribution is P(x) = (e^-λ * λ^x) / x!, where λ is the average number of events occurring in a given time period and x is the desired number of events.

How do you determine if a scenario follows a binomial or Poisson distribution?

A scenario follows a binomial distribution if it meets the following criteria: there are a fixed number of trials, each trial has two possible outcomes (success or failure), the probability of success remains constant for each trial, and the trials are independent. A scenario follows a Poisson distribution if it involves counting the number of events that occur in a specific time period or space, the events occur independently of each other, and the average number of events is known.

Can binomial and Poisson distributions be used to model real-world scenarios?

Yes, binomial and Poisson distributions can be used to model real-world scenarios. Binomial distributions are commonly used to model binary outcomes such as success or failure in a series of trials. Poisson distributions are often used to model the number of events occurring in a specific time period or space, such as the number of customers entering a store in an hour or the number of accidents on a particular road in a day.

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