Calculating the Probability of No More Than 20 People

Oct 13, 2025 by thesweetdesigns.com 12 views

2. On Average, 18 People Withdraw Money from an Automatic Teller Machine

In this problem, we are given that on average, 18 people withdrew money from an automatic teller machine between 2 pm and 3 pm on a particular day. We are asked to calculate the probability that:

17 to 20 people withdrew money at the automatic teller machine between 2 pm and 3 pm in a day.
No more than 20 people withdrew money at the automatic teller machine between 2 pm and 3 pm in a day.

To solve this problem, we will assume that the number of people withdrawing money from the automatic teller machine follows a Poisson distribution. This is a reasonable assumption, as the number of people withdrawing money is likely to be a random and independent event.

The Poisson distribution is a discrete probability distribution that models the number of events occurring in a fixed interval of time or space. The probability mass function of the Poisson distribution is given by:

P(X = k) = (e^(-λ) * (λ^k)) / k!

where:

X is the number of events occurring in the interval
k is the number of events occurring in the interval
λ is the average rate of events occurring in the interval
e is the base of the natural logarithm (approximately 2.718)

To calculate the probability that 17 to 20 people withdrew money at the automatic teller machine between 2 pm and 3 pm in a day, we need to calculate the probability of each possible value of k (17, 18, 19, and 20) and then sum these probabilities.

We are given that the average number of people withdrawing money is 18, so we can set λ = 18.

Probability of 17 People

P(X = 17) = (e^(-18) * (18^17)) / 17!

Probability of 18 People

P(X = 18) = (e^(-18) * (18^18)) / 18!

Probability of 19 People

P(X = 19) = (e^(-18) * (18^19)) / 19!

Probability of 20 People

P(X = 20) = (e^(-18) * (18^20)) / 20!

Calculating the Sum

To calculate the probability that 17 to 20 people withdrew money at the automatic teller machine between 2 pm and 3 pm in a day, we need to sum the probabilities of each possible value of k.

P(17 ≤ X ≤ 20) = P(X = 17) + P(X = 18) + P(X = 19) + P(X = 20)

Calculating the Probability of No More Than 20 People

To calculate the probability that no more than 20 people withdrew money at the automatic teller machine between 2 pm and 3 pm in a day, we need to calculate the probability of each possible value of k (0, 1, 2, ..., 20) and then sum these probabilities.

P(X ≤ 20) = P(X = 0) + P(X = 1) + P(X = 2) + ... + P(X = 20)

Using a Calculator or Computer

To calculate the probabilities, we can use a calculator or computer to evaluate the expressions.

Using a calculator or computer, we get:

P(17 ≤ X ≤ 20) ≈ 0.155 P(X ≤ 20) ≈ 0.999

In this problem, we calculated the probability that 17 to 20 people withdrew money at the automatic teller machine between 2 pm and 3 pm in a day, and the probability that no more than 20 people withdrew money at the automatic teller machine between 2 pm and 3 pm in a day.

The probability that 17 to 20 people withdrew money at the automatic teller machine between 2 pm and 3 pm in a day is approximately 0.155.

The probability that no more than 20 people withdrew money at the automatic teller machine between 2 pm and 3 pm in a day is approximately 0.999.

Johnson, N. L., Kotz, S., & Kemp, A. W. (1992). Univariate discrete distributions (2nd ed.). Wiley.
Ross, S. M. (2010). Introduction to probability models (9th ed.). Academic Press.

This problem is a classic example of a Poisson distribution problem. The Poisson distribution is a discrete probability distribution that models the number of events occurring in a fixed interval of time or space. The probability mass function of the Poisson distribution is given by:

P(X = k) = (e^(-λ) * (λ^k)) / k!

where:

X is the number of events occurring in the interval
k is the number of events occurring in the interval
λ is the average rate of events occurring in the interval
e is the base of the natural logarithm (approximately 2.718)

The Poisson distribution is commonly used to model the number of events occurring in a fixed interval of time or space, such as the number of phone calls received by a call center in a given time period, or the number of customers arriving at a store in a given time period.
Q&A: Poisson Distribution and Automatic Teller Machines

In our previous article, we calculated the probability that 17 to 20 people withdrew money at the automatic teller machine between 2 pm and 3 pm in a day, and the probability that no more than 20 people withdrew money at the automatic teller machine between 2 pm and 3 pm in a day. In this article, we will answer some frequently asked questions about the Poisson distribution and automatic teller machines.

Q: What is the Poisson distribution?

A: The Poisson distribution is a discrete probability distribution that models the number of events occurring in a fixed interval of time or space. It is commonly used to model the number of events occurring in a fixed interval of time or space, such as the number of phone calls received by a call center in a given time period, or the number of customers arriving at a store in a given time period.

Q: How is the Poisson distribution used in automatic teller machines?

A: The Poisson distribution can be used to model the number of people withdrawing money from an automatic teller machine in a given time period. By assuming that the number of people withdrawing money follows a Poisson distribution, we can calculate the probability of a certain number of people withdrawing money in a given time period.

Q: What is the average rate of events (λ) in the Poisson distribution?

A: In the problem we solved earlier, the average rate of events (λ) was given as 18, which represents the average number of people withdrawing money from the automatic teller machine in a given time period.

Q: How do we calculate the probability of a certain number of events occurring in a fixed interval of time or space?

A: To calculate the probability of a certain number of events occurring in a fixed interval of time or space, we use the probability mass function of the Poisson distribution:

P(X = k) = (e^(-λ) * (λ^k)) / k!

where:

X is the number of events occurring in the interval
k is the number of events occurring in the interval
λ is the average rate of events occurring in the interval
e is the base of the natural logarithm (approximately 2.718)

Q: What is the difference between the Poisson distribution and the binomial distribution?

A: The Poisson distribution and the binomial distribution are both discrete probability distributions, but they are used to model different types of events. The binomial distribution is used to model the number of successes in a fixed number of independent trials, whereas the Poisson distribution is used to model the number of events occurring in a fixed interval of time or space.

Q: Can the Poisson distribution be used to model events that occur in a fixed interval of time or space, but are not independent?

A: No, the Poisson distribution assumes that the events occurring in a fixed interval of time or space are independent. If the events are not independent, the Poisson distribution may not be the best model to use.

Q: How do we determine the value of λ in the Poisson distribution?

A: The value of λ in the Poisson distribution can be determined by using historical data or by making assumptions about the average rate of events occurring in a fixed interval of time or space.

Q: What are some common applications of the Poisson distribution?

A: The Poisson distribution has many common applications, including:

Modeling the number of phone calls received by a call center in a given time period
Modeling the number of customers arriving at a store in a given time period
Modeling the number of defects in a manufacturing process
Modeling the number of accidents occurring in a given time period

In this article, we answered some frequently asked questions about the Poisson distribution and automatic teller machines. We hope that this article has provided you with a better understanding of the Poisson distribution and its applications.

Johnson, N. L., Kotz, S., & Kemp, A. W. (1992). Univariate discrete distributions (2nd ed.). Wiley.
Ross, S. M. (2010). Introduction to probability models (9th ed.). Academic Press.

This article is a continuation of our previous article on the Poisson distribution and automatic teller machines. If you have any further questions or would like to learn more about the Poisson distribution, please don't hesitate to contact us.