What Is the Probability of 30 Car Accidents in Eight Random Days?

In summary, the problem involves calculating the probability of the total number of car accidents in a city in a given 8-day period, using the Poisson distribution. The average number of accidents per month is 125, leading to an expected value of 122 days in the given 4 months. The solution involves converting the monthly rate to a daily rate and then using the expected value for a single day to determine the probability for 8 days.
  • #1
Yankel
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Hello all, I have this Poisson distribution question, which I find slightly tricky, and I'll explain why.

The number of car accidents in a city has a Poisson distribution. In March the number was 150, in April 120, in May 110 and in June 120. Eight days are being chosen by random, not necessarily in the same month. What is the probability that the total number of accidents in the eight months will be 30 ?

What I thought to do, is to say that during this period, the average number of accidents is 125 a month, and therefore this is my [tex]\lambda[/tex] . Then I wanted to go from a monthly rate to a daily rate, and here comes the trick. How many days are in a month ? So I choose 30, and then the daily rate is [tex]\frac{100}{3}[/tex] , and so the required probability is 0.06. Am I making sense, or am I way off the direction in this one ? Thank you !
 
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  • #2
Hi Yankel,

How about taking $\lambda$ per day?
There are 122 days in the given 4 months.
 
  • #3
Yes, you are right, an expected value for a single day and from there to 8 days.

thanks !
 

FAQ: What Is the Probability of 30 Car Accidents in Eight Random Days?

What is a Poisson Distribution Problem?

A Poisson Distribution Problem is a mathematical problem that involves modeling the probability of rare events occurring within a specific time or space interval. It is named after French mathematician Siméon Denis Poisson and is commonly used in statistics and probability theory.

What are the characteristics of a Poisson Distribution?

A Poisson Distribution is characterized by the following properties:

  • The events occur independently of each other.
  • The average rate of events occurring is constant.
  • The probability of an event occurring in a given interval is proportional to the length of the interval.
  • The probability of more than one event occurring in a given interval is negligible.

How is a Poisson Distribution Problem solved?

A Poisson Distribution Problem is typically solved using the Poisson Distribution formula, which calculates the probability of a certain number of events occurring in a given time or space interval. The formula is: P(x) = (e^-λ*λ^x)/x!, where x is the number of events, and λ is the average rate of events occurring.

What are some real-life applications of Poisson Distribution?

Poisson Distribution has many real-life applications, including:

  • Modeling the number of customers arriving at a store within a given time period.
  • Predicting the number of accidents on a certain stretch of road in a given time frame.
  • Estimating the number of defects in a manufacturing process.
  • Calculating the number of calls received by a call center in a given time period.

How is a Poisson Distribution different from a Binomial Distribution?

The main difference between a Poisson Distribution and a Binomial Distribution is that a Poisson Distribution is used to model the probability of rare events occurring, while a Binomial Distribution is used to model the probability of a certain number of successes in a fixed number of trials. Additionally, a Poisson Distribution assumes a constant average rate of events occurring, while a Binomial Distribution allows for variation in the probability of success from trial to trial.

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