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For Poisson fitting to an observed frequency distribution,
- a)we equate the Poisson parameter to the mean of the frequency distribution.
- b)we equate the Poisson parameter to the median of the distribution.
- c)we equate the Poisson parameter to the mode of the distribution.
- d)none of these.
Correct answer is option 'A'. Can you explain this answer?
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For Poisson fitting to an observed frequency distribution,a)we equate ...
Explanation:
Poisson distribution is used to model the number of occurrences of a certain event in a fixed interval of time or space. It is a discrete probability distribution that takes only non-negative integer values.
Poisson fitting is a statistical technique used to fit a Poisson distribution to an observed frequency distribution. This technique involves estimating the parameter of the Poisson distribution that best describes the observed data.
The parameter of Poisson distribution is denoted by λ and represents the expected number of occurrences of the event in the given interval.
To perform a Poisson fitting to an observed frequency distribution, we need to estimate the value of λ. We can do this by equating λ to the mean of the frequency distribution.
Here are the steps involved in Poisson fitting to an observed frequency distribution:
1. Calculate the mean of the observed frequency distribution.
2. Equate λ to the mean of the frequency distribution.
3. Use the Poisson distribution formula to calculate the probability of observing each value in the frequency distribution.
4. Compare the calculated probabilities with the observed frequencies.
5. Use a goodness-of-fit test to determine if the Poisson distribution is a good fit for the observed data.
Option 'A' is the correct answer because we equate the Poisson parameter to the mean of the frequency distribution in Poisson fitting.
To summarize:
- Poisson fitting is a statistical technique used to fit a Poisson distribution to an observed frequency distribution.
- The parameter of Poisson distribution is denoted by λ and represents the expected number of occurrences of the event in the given interval.
- In Poisson fitting, we estimate the value of λ by equating it to the mean of the frequency distribution.
- We then use the Poisson distribution formula to calculate the probability of observing each value in the frequency distribution and compare it with the observed frequencies.
- Option 'A' is the correct answer because we equate the Poisson parameter to the mean of the frequency distribution in Poisson fitting.
Poisson distribution is used to model the number of occurrences of a certain event in a fixed interval of time or space. It is a discrete probability distribution that takes only non-negative integer values.
Poisson fitting is a statistical technique used to fit a Poisson distribution to an observed frequency distribution. This technique involves estimating the parameter of the Poisson distribution that best describes the observed data.
The parameter of Poisson distribution is denoted by λ and represents the expected number of occurrences of the event in the given interval.
To perform a Poisson fitting to an observed frequency distribution, we need to estimate the value of λ. We can do this by equating λ to the mean of the frequency distribution.
Here are the steps involved in Poisson fitting to an observed frequency distribution:
1. Calculate the mean of the observed frequency distribution.
2. Equate λ to the mean of the frequency distribution.
3. Use the Poisson distribution formula to calculate the probability of observing each value in the frequency distribution.
4. Compare the calculated probabilities with the observed frequencies.
5. Use a goodness-of-fit test to determine if the Poisson distribution is a good fit for the observed data.
Option 'A' is the correct answer because we equate the Poisson parameter to the mean of the frequency distribution in Poisson fitting.
To summarize:
- Poisson fitting is a statistical technique used to fit a Poisson distribution to an observed frequency distribution.
- The parameter of Poisson distribution is denoted by λ and represents the expected number of occurrences of the event in the given interval.
- In Poisson fitting, we estimate the value of λ by equating it to the mean of the frequency distribution.
- We then use the Poisson distribution formula to calculate the probability of observing each value in the frequency distribution and compare it with the observed frequencies.
- Option 'A' is the correct answer because we equate the Poisson parameter to the mean of the frequency distribution in Poisson fitting.
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For Poisson fitting to an observed frequency distribution,a)we equate ...
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For Poisson fitting to an observed frequency distribution,a)we equate the Poisson parameter to the mean of the frequency distribution.b)we equate the Poisson parameter to the median of the distribution.c)we equate the Poisson parameter to the mode of the distribution.d)none of these.Correct answer is option 'A'. Can you explain this answer?
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