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''Q''(3.9) = 0.000048096<br/>
''Q''(3.9) = 0.000048096<br/>
''Q''(4.0) = 0.000031671<br/>
''Q''(4.0) = 0.000031671<br/>
''Q''(4.0) = 0.000031672<br/>

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Revision as of 19:51, 12 August 2010

A plot of the Q-function.

In statistics, the Q-function is the tail probability of the standard normal distribution.[1][2] In other words, is the probability that a standard normal random variable will obtain a value larger than . Other definitions of the Q-function, all of which are simple transformations of the normal cumulative distribution function, are also used occasionally.[3]

Definition and basic properties

Formally, the Q-function is defined as

Thus,

where is the cumulative distribution function of the normal Gaussian distribution.

The Q-function can be expressed in terms of the error function as[2]

Bounds

The Q-function is not an elementary function. However, the bounds

become increasingly tight for large x, and are often useful.

Using the substitution and defining the upper bound is derived as follows:

Similarly, using and the quotient rule,

Solving for provides the lower bound.

Values

Some values of the Q-function are given below for reference.

See also

References