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65 Cards in this Set

  • Front
  • Back
Law of Total Probability
P[A] =
Bayes' Theorm
P[A|B] =
Probability Function
(discrete)
Probability Density Function
(continuous)
Cumulative Distribution Function
Discrete CDF
Continuous CDF
CDF to PDF
Hazard Rate h(x)
E[X]
(discrete)
E[X]
(continuous)
E[h(x)]
(discrete)
E[h(x)]
(continuous)
Variance of X
VAR[X]
Standard deviation of X
Moment Generating Function of X
Percentile of a Distribution
Mode of a Distribution
MAX [ f(x) ]
Skewness of a Distribution
VAR [ aX + b ]
Chebyshev's Inequality
p(x)
(discrete uniform)
E[x]
(discrete uniform)
VAR[X]
(discrete uniform)
Mx(t)
(discrete uniform)
p(x)
(discrete binomial)
E[X]
(discrete binomial)
np
VAR[X]
(discrete binomial)
np(1-p)
Mx(t)
(discrete binomial)
p(x)
(discrete Poisson)
E[x]
(discrete Poisson)
VAR[X]
(discrete Poisson)
Mx(t)
(discrete Poisson)
p(x)
(discrete Geometric)
E[X]
(discrete Geometric)
VAR[X]
(discrete Geometric)
Mx(t)
(discrete Geometric)
p(x)
(discrete negative binomial
E[X]
Negative Binomial
(Discrete)
VAR[X]
Negative Binomial
(Discrete)
Mx(t)
(discrete negative binomial)
p(x)
(continuous uniform)
E[X]
(continuous uniform)
VAR[X]
(continuous uniform)
Mx(t)
(continuous uniform)
p(x)
(continuous normal)
E[X]
(continuous normal)
VAR[X]
(continuous normal)
Mx(t)
(continuous normal)
p(x)
(continuous exponential)
E[X]
(continuous exponential)
VAR[X]
(continuous exponential)
Mx(t)
(continuous exponential)