# Q Function

In probability theory and statistics, the Q function is the tail probability of the standard normal distribution, also known as the complementary cumulative distribution function (CCDF) of the standard normal distribution. The Q function is defined as:

$Q(x) = \frac{1}{\sqrt{2\pi}} \int_{x}^{\infty} e^{-t^2\over2} dt$

where x is a real number.

The Q function calculates the probability that a random variable from a normal distribution with mean 0 and standard deviation 1 exceeds a certain value, $x$. This probability can be written as:

$P(Z>x) = Q(x)$

where Z is a standard normal variable.

The Q function can be calculated using numerical integration or special functions, such as the complementary error function or the Marcum Q-function.

The Q function is helpful in various applications, such as digital communications and signal processing, where it calculates error probabilities and signal-to-noise ratios. It is also used in statistics to calculate confidence intervals and hypothesis tests for normal distributions.