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| | Hi [http://Search.Un.org/search?ie=utf8&site=un_org&output=xml_no_dtd&client=UN_Website_en&num=10&lr=lang_en&proxystylesheet=UN_Website_en&oe=utf8&q=correct&Submit=Go correct]. Let me start by introducing the author, his name is Norris. Alabama has always been her residential. One of the things he loves most is ballet and they is doing make it a professional. After being out of his job for years he became a postal service worker. I'm not are able of webdesign an individual might for you to check my website: http://www.archive.org/details/miley_cyrus_playboy<br><br>Feel free to surf to my homepage [http://www.archive.org/details/miley_cyrus_playboy miley cyrus playboy] |
| [[Image:SkewedDistribution.png|thumb|200px||Example of experimental data with non-zero (positive) skewness (gravitropic response of [[wheat]] [[coleoptile]]s, 1,790)]]
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| In [[probability theory]] and [[statistics]], '''skewness''' is a measure of the asymmetry of the [[probability distribution]] of a [[real number|real]]-valued [[random variable]] about its mean. The skewness value can be positive or negative, or even undefined.
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| The qualitative interpretation of the skew is complicated. For a [[unimodal]] distribution, negative skew indicates that the ''tail'' on the left side of the probability density function is [[Long tail|longer]] or [[Fat-tailed distribution|fatter]] than the right side – it does not distinguish these shapes. Conversely, positive skew indicates that the tail on the right side is longer or fatter than the left side. In cases where one tail is long but the other tail is fat, skewness does not obey a simple rule. For example, a zero value indicates that the tails on both sides of the mean balance out, which is the case both for a symmetric distribution, and for asymmetric distributions where the asymmetries even out, such as one tail being long but thin, and the other being short but fat. Further, in multimodal distributions and discrete distributions, skewness is also difficult to interpret. Importantly, the skewness does not determine the [[#Relationship of mean and median|relationship of mean and median]].
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| ==Introduction==
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| Consider the distribution in the figure. The bars on the right side of the distribution taper differently than the bars on the left side. These tapering sides are called ''tails'', and they provide a visual means for determining which of the two kinds of skewness a distribution has:
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| #''{{visible anchor|negative skew}}'': The left tail is longer; the mass of the distribution is concentrated on the right of the figure. The distribution is said to be ''left-skewed'', ''left-tailed'', or ''skewed to the left''.<ref name="cnx.org">Susan Dean, Barbara Illowsky [http://cnx.org/content/m17104/latest/ "Descriptive Statistics: Skewness and the Mean, Median, and Mode"], Connexions website</ref> Example (observations): 1,1001,1002,1003.
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| #''{{visible anchor|positive skew}}'': The right tail is longer; the mass of the distribution is concentrated on the left of the figure. The distribution is said to be ''right-skewed'', ''right-tailed'', or ''skewed to the right''.<ref name="cnx.org"/> Example (observations): 1,2,3,1000.
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| ==Relationship of mean and median== | |
| The skewness is not strictly connected with the relationship between the mean and median: a distribution with negative skew can have the mean greater than or less than the median, and likewise for positive skew.
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| In the older notion of [[nonparametric skew]], defined as <math>(\mu - \nu)/\sigma,</math> where ''µ'' is the [[mean]], ''ν'' is the [[median]], and ''σ'' 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 the left of) the median. However, the modern definition of skewness and the traditional nonparametric definition do not in general have the same sign: while they agree for some families of distributions, they differ in general, and conflating them is misleading.
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| If the distribution is [[Symmetric probability distribution|symmetric]] then the mean is equal to the median and the distribution will have zero skewness.<ref>{{cite web|title=1.3.5.11. Measures of Skewness and Kurtosis |url=http://www.itl.nist.gov/div898/handbook/eda/section3/eda35b.htm|publisher=NIST|accessdate=18 March 2012}}</ref> If, in addition, the distribution is [[Unimodal distribution|unimodal]], then the [[mean]] = [[median]] = [[Mode (statistics)|mode]]. This is the case of a coin toss or the series 1,2,3,4,... Note, however, that the converse is not true in general, i.e. zero skewness does not imply that the mean is equal to the median.
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| "Many textbooks," a 2005 article points out, "teach a rule of thumb stating that the mean is right of the median under right skew, and left of the median under left skew. [But] this rule fails with surprising frequency. It can fail in [[multimodal distribution]]s, or in distributions where one tail is [[Long tail|long]] but the other is [[Fat-tailed distribution|fat]]. Most commonly, though, the rule fails in discrete distributions where the areas to the left and right of the median are not equal.{{clarify|date=November 2013}} Such distributions not only contradict the textbook relationship between mean, median, and skew, they also contradict the textbook interpretation of the median."<ref>{{cite journal|url=http://www.amstat.org/publications/jse/v13n2/vonhippel.html|title=Mean, Median, and Skew: Correcting a Textbook Rule|first=Paul T.|last=von Hippel|journal=Journal of Statistics Education| volume=13| issue=2| year=2005}}</ref>
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| [[Image:Negative and positive skew diagrams (English).svg]]
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| ==Definition==
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| The skewness of a random variable ''X'' is the third [[standardized moment]], denoted ''γ''<sub>1</sub> and defined as
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| : <math>
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| \gamma_1 = \operatorname{E}\Big[\big(\tfrac{X-\mu}{\sigma}\big)^{\!3}\, \Big]
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| = \frac{\mu_3}{\sigma^3}
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| = \frac{\operatorname{E}\big[(X-\mu)^3\big]}{\ \ \ ( \operatorname{E}\big[ (X-\mu)^2 \big] )^{3/2}}
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| = \frac{\kappa_3}{\kappa_2^{3/2}}\ ,
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| </math>
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| where ''μ''<sub>3</sub> is the third [[central moment]] ''μ'', ''σ'' is the [[standard deviation]], and ''E'' is the [[expected value|expectation operator]]. The last equality expresses skewness in terms of the ratio of the third [[cumulant]] ''κ''<sub>3</sub> and the 1.5th power of the second cumulant ''κ''<sub>2</sub>. This is analogous to the definition of [[kurtosis]] as the fourth cumulant normalized by the square of the second cumulant.
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| The skewness is also sometimes denoted Skew[''X''].
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| The formula expressing skewness in terms of the non-central moment E[''X''<sup>3</sup>] can be expressed by expanding the previous formula,
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| : <math>
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| \begin{align}
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| \gamma_1
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| &= \operatorname{E}\bigg[\Big(\frac{X-\mu}{\sigma}\Big)^{\!3} \,\bigg] \\
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| & = \frac{\operatorname{E}[X^3] - 3\mu\operatorname E[X^2] + 3\mu^2\operatorname E[X] - \mu^3}{\sigma^3}\\
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| &= \frac{\operatorname{E}[X^3] - 3\mu(\operatorname E[X^2] -\mu\operatorname E[X]) - \mu^3}{\sigma^3}\\
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| &= \frac{\operatorname{E}[X^3] - 3\mu\sigma^2 - \mu^3}{\sigma^3}\ .
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| \end{align}
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| </math>
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| <!-- EDITORS BEWARE: DO NOT CHANGE THIS INTO E[X^3] - 3\mu E[X^2] + 2\mu^3 /// SEE TALK PAGE -->
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| === Sample skewness ===
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| For a sample of ''n'' values the ''sample skewness'' is
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| : <math>
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| g_1 = \frac{m_3}{m_2^{3/2}}
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| = \frac{\tfrac{1}{n} \sum_{i=1}^n (x_i-\overline{x})^3}{\left(\tfrac{1}{n} \sum_{i=1}^n (x_i-\overline{x})^2\right)^{3/2}}\ ,
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| </math>
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| where <math>\scriptstyle\overline{x}</math> is the [[sample mean]], ''m''<sub>3</sub> is the sample third [[central moment]], and ''m''<sub>2</sub> is the [[sample variance]].
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| Given samples from a population, the equation for the sample skewness <math>g_1</math> above is a [[biased estimator]] of the population skewness. (Note that for a [[discrete distribution]] the sample skewness may be undefined (0/0), so its expected value will be undefined.) The usual estimator of population skewness is{{Citation needed|date=March 2010}}
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| : <math>
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| G_1 = \frac{k_3}{k_2^{3/2}} = \frac{\sqrt{n\,(n-1)}}{n-2}\; g_1,
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| </math>
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| where <math>k_3</math> is the unique symmetric unbiased estimator of the third [[cumulant]] and <math>k_2</math> is the symmetric unbiased estimator of the second cumulant. Unfortunately <math>G_1</math> is, nevertheless, generally biased (although it obviously has the correct expected value of zero for a symmetric distribution). Its expected value can even have the opposite sign from the true skewness. For instance a mixed distribution consisting of very thin Gaussians centred at −99, 0.5, and 2 with weights 0.01, 0.66, and 0.33 has a skewness of about −9.77, but in a sample of 3, <math>G_1</math> has an expected value of about 0.32, since usually all three samples are in the positive-valued part of the distribution, which is skewed the other way.
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| The variance of the skewness of a sample of size ''n'' from a [[normal distribution]] is<ref name=Duncan1997>Duncan Cramer (1997) Fundamental Statistics for Social Research. Routledge. ISBN13 9780415172042 (p 85)</ref><ref>Kendall, M.G.; Stuart, A. (1969) ''The Advanced Theory of Statistics, Volume 1: Distribution Theory, 3rd Edition'', Griffin. ISBN10 0-85264-141-9 (Ex 12.9)</ref>
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| : <math> \operatorname{var}(G_1)= \frac{6n ( n - 1 )}{ ( n - 2 )( n + 1 )( n + 3 ) } .</math>
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| An approximate alternative is 6/''n'' but this is inaccurate for small samples.
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| === Properties ===
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| Skewness can be infinite, as when
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| :<math>\Pr \left[ X > x \right]=x^{-3}\mbox{ for }x>1,\ \Pr[X<1]=0</math>
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| or undefined, as when
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| :<math>\Pr[X<x]=(1-x)^{-3}/2\mbox{ for negative }x\mbox{ and }\Pr[X>x]=(1+x)^{-3}/2\mbox{ for positive }x.</math>
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| In this latter example, the third cumulant is undefined. One can also have distributions such as
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| :<math>\Pr \left[ X > x \right]=x^{-2}\mbox{ for }x>1,\ \Pr[X<1]=0</math>
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| where both the second and third cumulants are infinite, so the skewness is again undefined.
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| If ''Y'' is the sum of ''n'' [[independent and identically distributed random variables]], all with the distribution of ''X'', then the third cumulant of ''Y'' is ''n'' times that of ''X'' and the second cumulant of ''Y'' is ''n'' times that of ''X'', so <math>\mbox{Skew}[Y] = \mbox{Skew}[X]/\sqrt{n}</math>. This shows that the skewness of the sum is smaller, as it approaches a Gaussian distribution in accordance with the [[central limit theorem]].
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| == Applications ==
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| Skewness has benefits in many areas. Many models assume normal distribution; i.e., data are symmetric about the mean. The normal distribution has a skewness of zero. But in reality, data points may not be perfectly symmetric. So, an understanding of the skewness of the dataset indicates whether deviations from the mean are going to be positive or negative.
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| [[D'Agostino's K-squared test]] is a [[goodness-of-fit]] [[normality test]] based on sample skewness and sample kurtosis.
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| In almost all countries the [[distribution of income]] is skewed to the right.
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| ==Other measures of skewness==
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| ===Pearson's skewness coefficients===
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| [[Image:Comparison mean median mode.svg|thumb|300px|Comparison of [[mean]], [[median]] and [[mode (statistics)|mode]] of two [[log-normal distribution]]s with different skewness.]]
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| [[Karl Pearson]] suggested simpler calculations as a measure of skewness:<ref>http://www.stat.upd.edu.ph/s114%20cnotes%20fcapistrano/Chapter%2010.pdf</ref> the Pearson mode or first skewness coefficient,<ref>{{MathWorld|title=Pearson Mode Skewness|urlname=PearsonModeSkewness}}</ref> defined by
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| * ([[mean]] − [[Mode (statistics)|mode]]) / [[standard deviation]],
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| as well as Pearson's median or second skewness coefficient,<ref>{{MathWorld|title=Pearson's skewness coefficients|urlname=PearsonsSkewnessCoefficients}}</ref> defined by
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| * 3 ([[mean]] − [[median]]) / [[standard deviation]].
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| The latter is a simple multiple of the [[nonparametric skew]].
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| Starting from a standard cumulant expansion around a Normal distribution, one can actually show that
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| skewness = 6 ([[mean]] − [[median]]) / [[standard deviation]] ( 1 + kurtosis / 8) + O(skewness<sup>2</sup>).{{Citation needed|date=April 2010}} One should keep in mind that above given equalities often don't hold even approximately and these empirical formulas are abandoned nowadays. There is no guarantee that these will be the same sign as each other or as the ordinary definition of skewness.
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| The adjusted Fisher-Pearson standardized moment coefficient is the version found in [[Microsoft Excel|Excel]] and several statistical packages including [[Minitab]], [[SAS (software)|SAS]] and [[SPSS]].<ref name=Doane2011>Doane DP, Seward LE (2011) J Stat Educ 19 (2)</ref> The formula for this statistic is
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| : <math> G = \frac{ n }{( n - 1 )( n - 2 ) } \sum_{ i = 1 }^n \left( \frac{ x_i - \overline{ x } }{ s } \right)^3 </math>
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| where ''n'' is the sample size and ''s'' is the sample standard deviation.
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| ===Quantile based measures===
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| A skewness function
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| :<math> \gamma( u )= \frac{ F^{ -1 }( u ) +F^{ -1 }( 1 - u )-2F^{ -1 }( 1 / 2 ) }{F^{ -1 }( u ) -F^{ -1 }( 1 - u ) } </math>
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| can be defined,<ref name=MacGillivray1992>MacGillivray (1992)</ref><ref name=Hinkley1975>Hinkley DV (1975) "On power transformations to symmetry", ''[[Biometrika]], 62, 101–111</ref> where ''F'' is the [[cumulative distribution function]]. This leads to a corresponding overall measure of skewness<ref name=MacGillivray1992>MacGillivray (1992)</ref> defined as the [[supremum]] of this over the range 1/2 ≤ ''u'' < 1. Another measure can be obtained by integrating the numerator and denominator of this expression.<ref name=Groeneveld1984/> The function ''γ''(''u'') satisfies -1 ≤ ''γ''(''u'') ≤ 1 and is well defined without requiring the existence of any moments of the distribution.<ref name=Groeneveld1984/>
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| Galton's measure of skewness<ref name=Johnson1994>Johnson ''et al'' (1994) p3, p40</ref> is γ(''u'') evaluated at ''u'' = 3 / 4. Other names for this same quantity are the Bowley Skewness,<ref name=Kenney1962>Kenney JF and Keeping ES (1962) ''Mathematics of Statistics, Pt. 1, 3rd ed.'', Van Nostrand, (page 102)</ref> the Yule-Kendall index<ref name=Wilks1995>Wilks DS (1995) ''Statistical Methods in the Atmospheric Sciences'', p27. Academic Press. ISBN 0-12-751965-3</ref> and the quartile skewness.
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| Kelley's measure of skewness uses u = 0.1.{{Citation needed|date=January 2012}}
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| ===L-moments===
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| Use of [[L-moment]]s in place of moments provides a measure of skewness known as the L-skewness.<ref name=hos:92>{{cite journal | last=Hosking | first= J.R.M. | year=1992 | title=Moments or L moments? An example comparing two measures of distributional shape | journal=The American Statistician | volume=46 | number=3 | pages=186–189 | jstor=2685210}}</ref>
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| === Cyhelský's skewness coefficient ===
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| An alternative skewness coefficient may be derived from the sample mean and the individual observations:<ref>{{cite web|title=Statistické charakteristiky (míry)|url=https://ilex.kin.tul.cz/~vladimira.valentova/multiedu/STI/Popisne_charakteristiky.pdf|publisher=[[Technical University of Liberec]]|accessdate=11 September 2012|page=6|language=Czech}}</ref>
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| : ''a'' = ( number of observations below the mean - number of observations above the mean ) / total number of observations
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| The distribution of the skewness coefficient ''a'' in large sample sizes (≥45) approaches that of a normal distribution. If the variates have a normal or a uniform distribution the distribution of ''a'' is the same. The behavior of ''a'' when the variates have other distributions is currently unknown. Although this measure of skewness is very intuitive, an analytic approach to its distribution has proven difficult.
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| === Distance skewness ===
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| A value of skewness equal to zero does not imply that the probability distribution is symmetric. Thus there is a need for another measure of asymmetry which has this property: such a measure was introduced in 2000.<ref>Szekely, G.J. (2000). "Pre-limit and post-limit theorems for statistics", In: ''Statistics for the 21st Century'' (eds. C. R. Rao and G. J. Szekely), Dekker, New York, pp. 411-422.</ref> It is called '''distance skewness''' and denoted by dSkew. If X is a random variable which takes values in the d-dimensional Euclidean space, X has finite expectation, X' is an independent identically distributed copy of X and <math>\|\cdot\|</math> denotes the norm in the Euclidean space then a simple ''measure of asymmetry'' is
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| :dSkew (X) := 1 - E||X-X'|| / E||X + X'|| if X is not 0 with probability one,
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| and dSkew (X):= 0 for X = 0 (with probability 1). Distance skewness is always between 0 and 1, equals 0 if and only if X is diagonally symmetric (X and -X has the same probability distribution) and equals 1 if and only if X is a nonzero constant with probability one.<ref>Szekely, G.J. and Mori, T.F. (2001) "A characteristic measure of asymmetry and its application for testing diagonal symmetry", ''Communications in Statistics - Theory and Methods'' 30/8&9, 1633–1639.</ref> Thus there is a simple consistent [[statistical test]] of diagonal symmetry based on the '''sample distance skewness''':
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| :dSkew<sub>n</sub>(X):= 1- ∑<sub>i,j</sub> ||x<sub>i</sub> – x<sub>j</sub>|| / ∑<sub>i,j</sub>||x<sub>i</sub> + x<sub>j</sub>||. | |
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| ===Groeneveld & Meeden’s coefficient===
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| Groeneveld & Meeden have suggested, as an alternative measure of skewness,<ref name=Groeneveld1984>{{Cite journal | doi = 10.2307/2987742 | last1 = Groeneveld | first1 = R.A. | last2 = Meeden | first2 = G. | year = 1984 | title = Measuring Skewness and Kurtosis | jstor = 2987742| journal = The Statistician | volume = 33 | issue = 4| pages = 391–399 }}
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| </ref>
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| : <math> \mathrm{skew}(X) = \frac{( \mu - \nu ) }{ E( | X - \nu | ) } </math>
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| where ''μ'' is the mean, ''ν'' is the median, |…| is the [[absolute value]] and ''E''() is the expectation operator.
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| == See also ==
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| {{commons category|Skewness (statistics)}}
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| *[[Bragg peak]]
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| *[[Shape parameter]]s
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| *[[Skew normal distribution]]
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| *[[Skewness risk]]
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| == Notes ==
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| {{Reflist}}
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| ==References==
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| *Johnson, NL, Kotz, S, Balakrishnan N (1994) ''Continuous Univariate Distributions, Vol 1, 2nd Edition'' Wiley ISBN 0-471-58495-9
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| *{{Cite journal | last1 = MacGillivray | first1 = HL | year = 1992 | title = Shape properties of the g- and h- and Johnson families | url = | journal = Comm. Statistics — Theory and Methods | volume = 21 | issue = | pages = 1244–1250 }}
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| == External links ==
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| {{wikiversity}}
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| *{{springer|title=Asymmetry coefficient|id=p/a013590}}
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| *[http://petitjeanmichel.free.fr/itoweb.petitjean.skewness.html An Asymmetry Coefficient for Multivariate Distributions] by Michel Petitjean
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| *[http://repositories.cdlib.org/cgi/viewcontent.cgi?article=1017&context=ucsdecon On More Robust Estimation of Skewness and Kurtosis] Comparison of skew estimators by Kim and White.
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| *[http://dahoiv.net/master/index.html Closed-skew Distributions — Simulation, Inversion and Parameter Estimation]
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| {{Statistics|descriptive}}
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| [[Category:Theory of probability distributions]]
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| [[Category:Statistical deviation and dispersion]]
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