Divide the total number of incidents by the total time. The result, representing the average number of incidents per unit of time, is the mean as well as the variance of the Poisson distribution.
The exponential distribution and the Poisson distribution.
The mean and variance are equal in the Poisson distribution. The mean and std deviation would be equal only for the case of mean = 1. See related link.
No. The variance of any distribution is the sum of the squares of the deviation from the mean. Since the square of the deviation is essentially the square of the absolute value of the deviation, that means the variance is always positive, be the distribution normal, poisson, or other.
Yes.
Why belong exponential family for poisson distribution
The exponential distribution and the Poisson distribution.
yes
It is a discrete distribution in which the men and variance have the same value.
The mean and variance are equal in the Poisson distribution. The mean and std deviation would be equal only for the case of mean = 1. See related link.
No. The variance of any distribution is the sum of the squares of the deviation from the mean. Since the square of the deviation is essentially the square of the absolute value of the deviation, that means the variance is always positive, be the distribution normal, poisson, or other.
The Poisson distribution. The Poisson distribution. The Poisson distribution. The Poisson distribution.
The Poisson distribution is discrete.
Yes.
Why belong exponential family for poisson distribution
we compute it by using their differences
Using the Taylor series expansion of the exponential function. See related links
The MGF is exp[lambda*(e^t - 1)].