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Probability generating functions are often employed for their succinct description of the sequence of probabilities Pr(X = i) in the probability mass function for a random variable X, and to make available the well-developed theory of power series with non-negative coefficients.
Given a random variable X ~ Norm[μ,σ] (a normal distribution with mean μ and standard deviation σ) and a constant L > μ, it can be shown via integration by substitution: [] = + (()) where A and B are certain numeric constants.
The log-normal distribution, describing variables which can be modelled as the product of many small independent positive variables. The Lomax distribution; The Mittag-Leffler distribution; The Nakagami distribution; The Pareto distribution, or "power law" distribution, used in the analysis of financial data and critical behavior.
The order of the power series f is defined to be the least value such that there is a α ≠ 0 with = | | = + + +, or if f ≡ 0. In particular, for a power series f(x) in a single variable x, the order of f is the smallest power of x with a nonzero coefficient.
About 68% of values drawn from a normal distribution are within one standard deviation σ from the mean; about 95% of the values lie within two standard deviations; and about 99.7% are within three standard deviations. [8] This fact is known as the 68–95–99.7 (empirical) rule, or the 3-sigma rule.
In probability theory and statistics, the F-distribution or F-ratio, also known as Snedecor's F distribution or the Fisher–Snedecor distribution (after Ronald Fisher and George W. Snedecor), is a continuous probability distribution that arises frequently as the null distribution of a test statistic, most notably in the analysis of variance (ANOVA) and other F-tests.
In probability theory and statistics, the noncentral F-distribution is a continuous probability distribution that is a noncentral generalization of the (ordinary) F-distribution. It describes the distribution of the quotient ( X / n 1 )/( Y / n 2 ), where the numerator X has a noncentral chi-squared distribution with n 1 degrees of freedom and ...
With reference to a continuous and strictly monotonic cumulative distribution function (c.d.f.) : [,] of a random variable X, the quantile function : [,] maps its input p to a threshold value x so that the probability of X being less or equal than x is p. In terms of the distribution function F, the quantile function Q returns the value x such that