cumulative distribution function
121probability theory — Math., Statistics. the theory of analyzing and making statements concerning the probability of the occurrence of uncertain events. Cf. probability (def. 4). [1830 40] * * * Branch of mathematics that deals with analysis of random events.… …
122Lorenz curve — The Lorenz curve is a graphical representation of the cumulative distribution function of a probability distribution; it is a graph showing the proportion of the distribution assumed by the bottom y % of the values. It is often used to represent… …
123Random variable — A random variable is a rigorously defined mathematical entity used mainly to describe chance and probability in a mathematical way. The structure of random variables was developed and formalized to simplify the analysis of games of chance,… …
124Bapat–Beg theorem — In probability theory, the Bapat–Beg theorem R. B. Bapat and M. I. Beg. Order statistics for nonidentically distributed variables and permanents. Sankhyā Ser. A , 51(1):79 ndash;93, 1989. [http://www.ams.org/mathscinet getitem?mr=1065561… …
125List of statistics topics — Please add any Wikipedia articles related to statistics that are not already on this list.The Related changes link in the margin of this page (below search) leads to a list of the most recent changes to the articles listed below. To see the most… …
126Exponential family — Not to be confused with the exponential distribution. Natural parameter links here. For the usage of this term in differential geometry, see differential geometry of curves. In probability and statistics, an exponential family is an important… …
127Q-Q plot — Not to be confused with P P plot. A normal Q Q plot of randomly generated, independent standard exponential data, (X   Exp(1)). This Q Q plot compares a sample of data on the vertical axis to a statistical population on the horizontal… …
128Central limit theorem — This figure demonstrates the central limit theorem. The sample means are generated using a random number generator, which draws numbers between 1 and 100 from a uniform probability distribution. It illustrates that increasing sample sizes result… …
