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both the binomial distribution and the poisson distribution are used to model/describe count data
the binomial variable is the total number of successes in n trials where the trials are independent with a constant probability of success denoted by p for each of them
the poisson distribution is the number of successes/responses in a defined time period or space where the rate per space unit or time unit is denoted by (λ)
the binomial variable has a maximum value which is the number of trials (n), while the poisson variable, in theory, does not have that upper limit
both binomial and poisson variables are example of
discrete variables
if Y has a poisson distribution with average rate λ \n (that is Y ~ Poisson(λ)) then what is the:
mean of y
variance of y
standard deviation of y
λ
λ
√λ
point probability → =POISSON.DIST (k, λ, 0)
cumulative probability → =POISSON.DIST (k, λ, 1)