Q Function

In probability theory and statistics, the Q function is the tail probability of the [standard normal](./Standard Normal.md) distribution, also known as the complementary cumulative distribution function (CCDF) of the [standard normal](./Standard Normal.md) 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](./Random Variable.md) 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](./Standard Normal.md) 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.