# Cumulative distribution function

## Overview

In probability theory, the **cumulative distribution function** (CDF), also called **probability distribution function** or just **distribution function**,^{[1]} completely describes the probability distribution of a real-valued random variable *X*. For every real number *x*, the CDF of X is given by

- <math>x \to F_X(x) = \operatorname{P}(X\leq x),</math>

where the right-hand side represents the probability that the random variable *X* takes on a value less than or
equal to *x*. The probability that *X* lies in the interval (*a*, *b*] is therefore *F*(*b*) − *F*(*a*) if *a* < *b*. It is conventional to use a capital *F* for a cumulative distribution function, in contrast to the lower-case *f* used for probability density functions and probability mass functions.

The CDF of X can be defined in terms of the probability density function *f* as follows:

- <math>F(x) = \int_{-\infty}^x f(t)\,dt</math>

Note that in the definition above, the "less or equal" sign, '≤' is a convention, but it is an important and universally used one. The proper use of tables of the Binomial and Poisson distributions depend upon this convention. Moreover, important formulas like Levy's inversion formula for the characteristic function also rely on the "less or equal" formulation.

== Properties ==leftright|thumb|From top to bottom, the cumulative distribution function of a discrete probability distribution, continuous probability distribution, and a distribution which has both a continuous part and a discrete part.]]
Every cumulative distribution function *F* is (not necessarily strictly) monotone increasing and right-continuous. Furthermore, we have

- <math>\lim_{x\to -\infty}F(x)=0, \quad \lim_{x\to +\infty}F(x)=1.</math>

Every function with these four properties is a cdf. The properties imply that all CDFs are càdlàg functions.

If *X* is a discrete random variable, then it attains values *x*_{1}, *x*_{2}, ... with probability *p*_{i} = P(*x*_{i}), and the cdf of *X* will be discontinuous at the points *x*_{i} and constant in between:

- <math>F(x) = \operatorname{P}(X\leq x) = \sum_{x_i \leq x} \operatorname{P}(X = x_i) = \sum_{x_i \leq x} p(x_i)</math>

If the CDF *F* of *X* is continuous, then *X* is a continuous random variable; if furthermore *F* is absolutely continuous, then there exists a Lebesgue-integrable function *f*(*x*) such that

- <math>F(b)-F(a) = \operatorname{P}(a\leq X\leq b) = \int_a^b f(x)\,dx</math>

for all real numbers *a* and *b*. (The first of the two equalities displayed above would not be correct in general if we had not said that the distribution is continuous. Continuity of the distribution implies that P(*X* = *a*) = P(*X* = *b*) = 0, so the difference between "<" and "≤" ceases to be important in this context.) The function *f* is equal to the derivative of *F* almost everywhere, and it is called the probability density function of the distribution of *X*.

### Point probability

The "point probability" that *X* is exactly *b* can be found as

- <math>\operatorname{P}(X=b) = F(b) - \lim_{x \to b^{-}} F(x)</math>

## Kolmogorov-Smirnov and Kuiper's tests

The Kolmogorov-Smirnov test is based on cumulative distribution functions and can be used to test to see whether two empirical distributions are different or whether an empirical distribution is different from an ideal distribution. The closely related Kuiper's test (pronounced Template:IPA) is useful if the domain of the distribution is cyclic as in day of the week. For instance we might use Kuiper's test to see if the number of tornadoes varies during the year or if sales of a product vary by day of the week or day of the month.

## Complementary cumulative distribution function

Sometimes, it is useful to study the opposite question and ask how often the random variable is *above* a particular level. This is called the **complementary cumulative distribution function** (**ccdf**), defined as

- <math>F_c(x) = \operatorname{P}(X > x) = 1 - F(x)</math>.

In survival analysis, <math>F_c(x)</math> is called the **survival function** and denoted <math> S(x) </math>.

## Examples

As an example, suppose *X* is uniformly distributed on the unit interval [0, 1].
Then the CDF of X is given by

- <math>F(x) = \begin{cases}

0 &:\ x < 0\\ x &:\ 0 \le x \le 1\\ 1 &:\ 1 < x \end{cases}</math>

Take another example, suppose *X* takes only the discrete values 0 and 1, with equal probability.
Then the CDF of X is given by

- <math>F(x) = \begin{cases}

0 &:\ x < 0\\ 1/2 &:\ 0 \le x < 1\\ 1 &:\ 1 \le x \end{cases}</math>

## Inverse

If the cdf *F* is strictly increasing and continuous then <math> F^{-1}( y ), y \in [0,1] </math> is the unique real number <math> x </math> such that <math> F(x) = y </math>.

Unfortunately, the distribution does not, in general, have an inverse. One may define, for <math> y \in [0,1] </math>,

- <math>

F^{-1}( y ) = \inf_{r \in \mathbb{R}} \{ F( r ) > y \} </math>.

Example 1: The median is <math>F^{-1}( 0.5 )</math>.

Example 2: Put <math> \tau = F^{-1}( 0.95 ) </math>. Then we call <math> \tau </math> the 95th percentile.

The inverse of the cdf is called the quantile function.

## See also

- Descriptive statistics
- Probability distribution
- Empirical distribution function
- Cumulative frequency distributions
- Q-Q plot
- Ogive
- Quantile function

## References

- ↑ Eric W. Weisstein.
*Distribution Function.*From MathWorld—A Wolfram Web Resource.

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