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Cumulative distribution function for the exponential distribution Cumulative distribution function for the normal distribution. In probability theory and statistics, the cumulative distribution function (CDF) of a real-valued random variable, or just distribution function of , evaluated at , is the probability that will take a value less than or equal to .
Computable Document Format (CDF) is an electronic document format [1] designed to allow authoring dynamically generated, interactive content. [2] CDF was created by Wolfram Research , and CDF files can be created using Mathematica . [ 3 ]
In statistics, an empirical distribution function (commonly also called an empirical cumulative distribution function, eCDF) is the distribution function associated with the empirical measure of a sample. [1] This cumulative distribution function is a step function that jumps up by 1/n at each of the n data points. Its value at any specified ...
When only the equality of the two groups means is in question (i.e. whether μ 1 = μ 2), the studentized range distribution is similar to the Student's t distribution, differing only in that the first takes into account the number of means under consideration, and the critical value is adjusted accordingly. The more means under consideration ...
In probability and statistics, the reciprocal distribution, also known as the log-uniform distribution, is a continuous probability distribution.It is characterised by its probability density function, within the support of the distribution, being proportional to the reciprocal of the variable.
The graph on the left is the cumulative distribution function, which is P(T ≤ t). The graph on the right is P(T > t) = 1 - P(T ≤ t). The graph on the right is the survival function, S(t). The fact that the S(t) = 1 – CDF is the reason that another name for the survival function is the complementary cumulative distribution function.
The figure shows the variation that may occur when obtaining samples of a variate that follows a certain probability distribution. The data were provided by Benson. [1] The confidence belt around an experimental cumulative frequency or return period curve gives an impression of the region in which the true distribution may be found.
In probability and statistics, the Kumaraswamy's double bounded distribution is a family of continuous probability distributions defined on the interval (0,1). It is similar to the beta distribution, but much simpler to use especially in simulation studies since its probability density function, cumulative distribution function and quantile functions can be expressed in closed form.