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In the older notion of nonparametric skew, defined as () /, where is the mean, is the median, and is the standard deviation, the skewness is defined in terms of this relationship: positive/right nonparametric skew means the mean is greater than (to the right of) the median, while negative/left nonparametric skew means the mean is less than (to ...
Skew lines, neither parallel nor intersecting.; Skew normal distribution, a probability distribution; Skew field or division ring; Skew-Hermitian matrix; Skew lattice; Skew polygon, whose vertices do not lie on a plane
The exponentially modified normal distribution is another 3-parameter distribution that is a generalization of the normal distribution to skewed cases. The skew normal still has a normal-like tail in the direction of the skew, with a shorter tail in the other direction; that is, its density is asymptotically proportional to for some positive .
In statistics and probability theory, the nonparametric skew is a statistic occasionally used with random variables that take real values. [ 1 ] [ 2 ] It is a measure of the skewness of a random variable's distribution —that is, the distribution's tendency to "lean" to one side or the other of the mean .
A configuration of skew lines is a set of lines in which all pairs are skew. Two configurations are said to be isotopic if it is possible to continuously transform one configuration into the other, maintaining throughout the transformation the invariant that all pairs of lines remain skew.
In algebra, a division ring, also called a skew field (or, occasionally, a sfield [1] [2]), is a nontrivial ring in which division by nonzero elements is defined. Specifically, it is a nontrivial ring [ 3 ] in which every nonzero element a has a multiplicative inverse , that is, an element usually denoted a –1 , such that a a –1 = a –1 a ...
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where Mean is the mean, StdDev is the standard deviation, Skew is the skewness, Kurt is the kurtosis and φ x is the value of the variate φ at the x th percentage of the distribution. Unimodal vs. bimodal distribution