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
Range of a Geometric random variable
0 < p <= 1
k ∈ {1, 2, 3,…}
pmf of a Geometric random variable
(1 - p)k - 1p
E[X] of a Negative Binomial
E[X] = (pr)/(1 - p)
Var(X) of a Negative Binomial
(pr)/(1 - p)2
Parameters for Negative Binomial
r — number of failures until the experiment is stopped
p — success probability in each experiment
k — number of successes
Range of Negative Binomial
r > 0
p ∈ (0,1)
k ∈ { 0, 1, 2, 3, … }
pmf of Negative Binomial
((k + r - 1) choose k) · (1 - p)rpk