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In statistics, the Q-function is the tail distribution function of the standard normal distribution. 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 . If is a Gaussian random variable with mean and variance , then is standard normal and where . Other definitions of the Q-function, all of which are simple transformations of the normal cumulative distribution function, are also used occasionally.

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  • Q-function (en)
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  • In statistics, the Q-function is the tail distribution function of the standard normal distribution. 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 . If is a Gaussian random variable with mean and variance , then is standard normal and where . Other definitions of the Q-function, all of which are simple transformations of the normal cumulative distribution function, are also used occasionally. (en)
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  • In statistics, the Q-function is the tail distribution function of the standard normal distribution. 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 . If is a Gaussian random variable with mean and variance , then is standard normal and where . Other definitions of the Q-function, all of which are simple transformations of the normal cumulative distribution function, are also used occasionally. Because of its relation to the cumulative distribution function of the normal distribution, the Q-function can also be expressed in terms of the error function, which is an important function in applied mathematics and physics. (en)
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