Chapter 4 Flashcards
If X is a discrete random variable having a probability mass function p(x), then the expectation, or the expected value, of X, denoted by E[X], is defined by
The sum such that x:p(x)>0 of xp(x)
If X is a random variable with mean E[X], then the variance of X, denoted by Var(X), is defined by
Var(X) = E[X2] − (E[X])2
E[X] of a Poisson random variable
E[X] = λ
Var(X) of a Poisson random variable
Var(X) = λ
Parameter for a Poisson random variable
λ
Range of a Poisson random variable
λ > 0
pmf of a Poisson random variable
p(k) = P{X = k} = (λk/k!)e-λ
E[X] of a Binomial random variable
E[X] = np
Var(X) of a Binomial random variable
Var(X) = np(1 - p)
Parameters for a Binomial random variable
n — number of trials
p — success probability in each trial
Range of a Binomial random variable
n ∈ N0
p ∈ [0,1]
pmf of a Binomial random variable
(n k) pk (1 - p)n - k
E[X] of a Geometric random variable
E[X] = 1/p
Var(X) of a Geometric random variable
Var(X) = (1 - p)/p2
Parameters of a Geometric random variable
p - probability
k - trials