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Cumulative distribution function for the exponential distribution Cumulative distribution function for the normal distribution. In probability theory and statistics, the cumulative distribution function (CDF) of a real-valued random variable, or just distribution function of , evaluated at , is the probability that will take a value less than or equal to .
The complement of the standard normal cumulative distribution function, () = (), is often called the Q-function, especially in engineering texts. [ 13 ] [ 14 ] It gives the probability that the value of a standard normal random variable X {\textstyle X} will exceed x {\textstyle x} : P ( X > x ) {\textstyle P(X>x)} .
The normal distribution is perhaps the most important case. Because the normal distribution is a location-scale family, its quantile function for arbitrary parameters can be derived from a simple transformation of the quantile function of the standard normal distribution, known as the probit function. Unfortunately, this function has no closed ...
In statistics, a standard normal table, also called the unit normal table or Z table, [1] is a mathematical table for the values of Φ, the cumulative distribution function of the normal distribution.
and Φ is the cumulative distribution function of the standard normal distribution. Alternatively, the noncentral t -distribution CDF can be expressed as [ citation needed ] : F v , μ ( x ) = { 1 2 ∑ j = 0 ∞ 1 j !
[1] [2] In other words, () is the probability that a normal (Gaussian) random variable will obtain a value larger than standard deviations. Equivalently, () is the probability that a standard normal random variable takes a value larger than .
Here F X is the cumulative distribution function of X, f X is the corresponding probability density function, Q X (p) is the corresponding inverse cumulative distribution function also called the quantile function, [2] and the integrals are of the Riemann–Stieltjes kind.
In order to create the studentized range distribution for normal data, we first switch from the generic f X and F X to the distribution functions φ and Φ for the standard normal distribution, and change the variable r to s·q, where q is a fixed factor that re-scales r by scaling factor s: