univariate skewness

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• Skewness — Example of experimental data with non zero (positive) skewness (gravitropic response of wheat coleoptiles, 1,790) In probability theory and statistics, skewness is a measure of the asymmetry of the probability distribution of a real valued random …   Wikipedia

• Geometric stable distribution — Geometric Stable parameters: α ∈ (0,2] stability parameter β ∈ [−1,1] skewness parameter (note that skewness is undefined) λ ∈ (0, ∞) scale parameter μ ∈ (−∞, ∞) location parameter support: x ∈ R, or x ∈ [μ, +∞) if α < 1 and β = 1, or x ∈… …   Wikipedia

• Noncentral t-distribution — Noncentral Student s t Probability density function parameters: degrees of freedom noncentrality parameter support …   Wikipedia

• Multivariate normal distribution — MVN redirects here. For the airport with that IATA code, see Mount Vernon Airport. Probability density function Many samples from a multivariate (bivariate) Gaussian distribution centered at (1,3) with a standard deviation of 3 in roughly the… …   Wikipedia

• Normal distribution — This article is about the univariate normal distribution. For normally distributed vectors, see Multivariate normal distribution. Probability density function The red line is the standard normal distribution Cumulative distribution function …   Wikipedia

• Multivariate stable distribution — multivariate stable Probability density function Heatmap showing a Multivariate (bivariate) stable distribution with α = 1.1 parameters: exponent shift/location vector …   Wikipedia

• Chi-squared distribution — This article is about the mathematics of the chi squared distribution. For its uses in statistics, see chi squared test. For the music group, see Chi2 (band). Probability density function Cumulative distribution function …   Wikipedia

• Normal-inverse Gaussian distribution — Normal inverse Gaussian (NIG) parameters: μ location (real) α tail heavyness (real) β asymmetry parameter (real) δ scale parameter (real) support …   Wikipedia

• Central moment — In probability theory and statistics, central moments form one set of values by which the properties of a probability distribution can be usefully characterised. Central moments are used in preference to ordinary moments because then the values… …   Wikipedia

• Fisher's noncentral hypergeometric distribution — Probability mass function for Fisher s noncentral hypergeometric distribution for different values of the odds ratio ω. m 1 = 80, m 2 = 60, n = 100, ω = 0.01, ..., 1000In probability theory and statistics, Fisher s noncentral hypergeometric… …   Wikipedia

• Wallenius' noncentral hypergeometric distribution — Introduction Probability mass function for Wallenius Noncentral Hypergeometric Distribution for different values of the odds ratio ω. m1 = 80, m2 = 60, n = 100, ω = 0.1 ... 20In probability theory and statistics, Wallenius noncentral… …   Wikipedia