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A function problem consists of a partial function f; the informal "problem" is to compute the values of f on the inputs for which it is defined. Every function problem can be turned into a decision problem; the decision problem is just the graph of the associated function. (The graph of a function f is the set of pairs (x,y) such that f(x) = y ...
Deviations from a straight line suggest departures from normality. The plotting can be manually performed by using a special graph paper, called normal probability paper. With modern computers normal plots are commonly made with software. The normal probability plot is a special case of the Q–Q probability plot for a normal distribution.
The function T(h, a) gives the probability of the event (X > h and 0 < Y < aX) where X and Y are independent standard normal random variables. This function can be used to calculate bivariate normal distribution probabilities [ 2 ] [ 3 ] and, from there, in the calculation of multivariate normal distribution probabilities. [ 4 ]
Statistical graphics have been central to the development of science and date to the earliest attempts to analyse data. Many familiar forms, including bivariate plots, statistical maps, bar charts, and coordinate paper were used in the 18th century. Statistical graphics developed through attention to four problems: [3]
The quadratic scoring rule is a strictly proper scoring rule (,) = = =where is the probability assigned to the correct answer and is the number of classes.. The Brier score, originally proposed by Glenn W. Brier in 1950, [4] can be obtained by an affine transform from the quadratic scoring rule.
Graphs of probability P of not observing independent events each of probability p after n Bernoulli trials vs np for various p.Three examples are shown: Blue curve: Throwing a 6-sided die 6 times gives a 33.5% chance that 6 (or any other given number) never turns up; it can be observed that as n increases, the probability of a 1/n-chance event never appearing after n tries rapidly converges to ...
In statistics, the Q-function is the tail distribution function of the standard normal distribution. [ 1 ] [ 2 ] In other words, Q ( x ) {\displaystyle Q(x)} is the probability that a normal (Gaussian) random variable will obtain a value larger than x {\displaystyle x} standard deviations.
This article needs attention from an expert in statistics. The specific problem is: completion to reasonable standard for probability distributions. WikiProject Statistics may be able to help recruit an expert.