CA Foundation Exam > CA Foundation Videos > Quantitative Aptitude for CA Foundation > Poisson Distribution
Poisson Distribution Video Lecture | Quantitative Aptitude for CA Foundation
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FAQs on Poisson Distribution Video Lecture - Quantitative Aptitude for CA Foundation
| 1. What is the Poisson distribution? |  |
Ans. The Poisson distribution is a probability distribution that is used to model the number of events that occur in a fixed interval of time or space. It is often used when the events occur independently and at a constant average rate.
| 2. How is the Poisson distribution calculated? | |
Ans. The Poisson distribution is calculated using the formula P(x; λ) = (e^(-λ) * λ^x) / x!, where x represents the number of events, λ is the average rate of events, e is Euler's number (approximately 2.71828), and x! denotes the factorial of x.
| 3. What are the characteristics of the Poisson distribution? | |
Ans. The Poisson distribution has several key characteristics. Firstly, it is discrete, meaning that it can only take on integer values. Secondly, it is positively skewed, with a longer tail on the right side. Lastly, the mean and variance of the Poisson distribution are equal and are both equal to the average rate parameter λ.
| 4. In what situations can the Poisson distribution be applied? | |
Ans. The Poisson distribution can be applied in various scenarios, such as estimating the number of customer arrivals in a given time period, analyzing the number of accidents on a road, predicting the number of phone calls received by a call center, or determining the number of emails received per day.
| 5. How does the Poisson distribution differ from other probability distributions? | |
Ans. The Poisson distribution differs from other probability distributions in several ways. Unlike the binomial distribution, which models the number of successes in a fixed number of trials, the Poisson distribution models the number of events in a fixed interval of time or space. Additionally, the Poisson distribution does not have a fixed number of trials, as it is applicable to situations where the number of events can vary.
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Nov 21, 2024 Last updated |
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