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In probability theory and statistics, the cumulative distribution function (CDF), or just distribution function, describes the probability that a realvalued random variable X with a given probability distribution will be found to have a value less than or equal to x. In the case of a continuous distribution, it gives the area under the probability density function from minus infinity to x. Cumulative distribution functions are also used to specify the distribution of multivariate random variables.
The cumulative distribution function of a realvalued random variable X is the function given by
where the righthand 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 semiclosed interval (a, b], where a < b, is therefore
In the definition above, the "less than or equal to" sign, "≤", is a convention, not a universally used one (e.g. Hungarian literature uses "<"), but is important for discrete distributions. The proper use of tables of the binomial and Poisson distributions depends upon this convention. Moreover, important formulas like Paul Lévy's inversion formula for the characteristic function also rely on the "less than or equal" formulation.
If treating several random variables X, Y, ... etc. the corresponding letters are used as subscripts while, if treating only one, the subscript is usually omitted. It is conventional to use a capital F for a cumulative distribution function, in contrast to the lowercase f used for probability density functions and probability mass functions. This applies when discussing general distributions: some specific distributions have their own conventional notation, for example the normal distribution.
The CDF of a continuous random variable X can be expressed as the integral of its probability density function ƒ_{X} as follows:
In the case of a random variable X which has distribution having a discrete component at a value b,
If F_{X} is continuous at b, this equals zero and there is no discrete component at b.
Every cumulative distribution function F is nondecreasing and rightcontinuous, which makes it a càdlàg function. Furthermore,
Every function with these four properties is a CDF, i.e., for every such function, a random variable can be defined such that the function is the cumulative distribution function of that random variable.
If X is a purely 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:
If the CDF F of a real valued random variable X is continuous, then X is a continuous random variable; if furthermore F is absolutely continuous, then there exists a Lebesgueintegrable function f(x) such that
for all real numbers a and b. 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.
As an example, suppose is uniformly distributed on the unit interval [0, 1]. Then the CDF of is given by
Suppose instead that takes only the discrete values 0 and 1, with equal probability. Then the CDF of is given by
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) or simply the tail distribution or exceedance, and is defined as
This has applications in statistical hypothesis testing, for example, because the onesided pvalue is the probability of observing a test statistic at least as extreme as the one observed. Thus, provided that the test statistic, T, has a continuous distribution, the onesided pvalue is simply given by the ccdf: for an observed value t of the test statistic
In survival analysis, is called the survival function and denoted , while the term reliability function is common in engineering.
While the plot of a cumulative distribution often has an Slike shape, an alternative illustration is the folded cumulative distribution or mountain plot, which folds the top half of the graph over,^{[2]}^{[3]} thus using two scales, one for the upslope and another for the downslope. This form of illustration emphasises the median and dispersion (the mean absolute deviation from the median^{[4]}) of the distribution or of the empirical results.
If the CDF F is strictly increasing and continuous then is the unique real number such that . In such a case, this defines the inverse distribution function or quantile function.
Unfortunately, the distribution does not, in general, have an inverse. One may define, for , the generalized inverse distribution function: ^{[clarification needed]}
The inverse of the cdf is called the quantile function.
The inverse of the cdf can be used to translate results obtained for the uniform distribution to other distributions. Some useful properties of the inverse cdf are:
When dealing simultaneously with more than one random variable the joint cumulative distribution function can also be defined. For example, for a pair of random variables X,Y, the joint CDF is given by
where the righthand side represents the probability that the random variable X takes on a value less than or equal to x and that Y takes on a value less than or equal to y.
Every multivariate CDF is:
The concept of the cumulative distribution function makes an explicit appearance in statistical analysis in two (similar) ways. Cumulative frequency analysis is the analysis of the frequency of occurrence of values of a phenomenon less than a reference value. The empirical distribution function is a formal direct estimate of the cumulative distribution function for which simple statistical properties can be derived and which can form the basis of various statistical hypothesis tests. Such tests can assess whether there is evidence against a sample of data having arisen from a given distribution, or evidence against two samples of data having arisen from the same (unknown) population distribution.
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 is useful if the domain of the distribution is cyclic as in day of the week. For instance Kuiper's test might be used 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.
