Covariance
Contenu
Titre (dcterms:title)
fr
Covariance
en
Covariance
Identifiant (dcterms:identifier)
definition (obo:IAO_0000115)
en
<p>The covariance is a measurement data item about the strength of correlation between a set (2 or more) of random variables. The covariance is obtained by forming :</p>
<p><a href="https://www.codecogs.com/eqnedit.php?latex=cov(X,Y)=\sum_{i=1}^{N}\frac{(X_{i}-\overline{X})(Y_{i}-\overline{Y})}{N}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?cov(X,Y)=\sum_{i=1}^{N}\frac{(X_{i}-\overline{X})(Y_{i}-\overline{Y})}{N}" title="cov(X,Y)=\sum_{i=1}^{N}\frac{(X_{i}-\overline{X})(Y_{i}-\overline{Y})}{N}" /></a><br />
where <a href="https://www.codecogs.com/eqnedit.php?latex=\overline{X}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\overline{X}" title="\overline{X}" /></a> , <a href="https://www.codecogs.com/eqnedit.php?latex=\overline{Y}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\overline{Y}" title="\overline{Y}" /></a> is the expected value (mean) of variable X and Y respectively. Covariance is symmetric so cov(X,Y)=cov(Y,X). The covariance is usefull when looking at the variance of the sum of the 2 random variables since : var(X+Y) = var(X) +var(Y) +2cov(X,Y). The covariance cov(x,y) is used to obtain the coefficient of correlation cor(x,y) by normalizing (dividing) cov(x,y) but the product of the standard deviations of x and y.</p>
<p><a href="https://www.codecogs.com/eqnedit.php?latex=cov(X,Y)=\sum_{i=1}^{N}\frac{(X_{i}-\overline{X})(Y_{i}-\overline{Y})}{N}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?cov(X,Y)=\sum_{i=1}^{N}\frac{(X_{i}-\overline{X})(Y_{i}-\overline{Y})}{N}" title="cov(X,Y)=\sum_{i=1}^{N}\frac{(X_{i}-\overline{X})(Y_{i}-\overline{Y})}{N}" /></a><br />
where <a href="https://www.codecogs.com/eqnedit.php?latex=\overline{X}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\overline{X}" title="\overline{X}" /></a> , <a href="https://www.codecogs.com/eqnedit.php?latex=\overline{Y}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\overline{Y}" title="\overline{Y}" /></a> is the expected value (mean) of variable X and Y respectively. Covariance is symmetric so cov(X,Y)=cov(Y,X). The covariance is usefull when looking at the variance of the sum of the 2 random variables since : var(X+Y) = var(X) +var(Y) +2cov(X,Y). The covariance cov(x,y) is used to obtain the coefficient of correlation cor(x,y) by normalizing (dividing) cov(x,y) but the product of the standard deviations of x and y.</p>
fr
<p>La covariance est une donnée de mesure de la force de corrélation entre un ensemble (2 ou plus) de variables aléatoires. La covariance est obtenue par la formule :</p>
<p><a href="https://www.codecogs.com/eqnedit.php?latex=cov(X,Y)=\sum_{i=1}^{N}\frac{(X_{i}-\overline{X})(Y_{i}-\overline{Y})}{N}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?cov(X,Y)=\sum_{i=1}^{N}\frac{(X_{i}-\overline{X})(Y_{i}-\overline{Y})}{N}" title="cov(X,Y)=\sum_{i=1}^{N}\frac{(X_{i}-\overline{X})(Y_{i}-\overline{Y})}{N}" /></a><br />
où <a href="https://www.codecogs.com/eqnedit.php?latex=\overline{X}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\overline{X}" title="\overline{X}" /></a> , <a href="https://www.codecogs.com/eqnedit.php?latex=\overline{Y}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\overline{Y}" title="\overline{Y}" /></a> est la valeur attendue (moyenne) des variables X et Y respectivement. </p>
<p>covariance est symétrique donc cov(X,Y)=cov(Y,X).</p>
<p>La covariance est utile lorsque l'on regarde la variance de la somme des 2 variables aléatoires depuis : var(X+Y) = var(X) +var(Y) +2cov(X,Y). La covariance cov(x,y) est utilisée pour obtenir le coefficient de corrélation cor(x,y) en normalisant (divisant) cov(x,y) mais le produit des écarts types de x et y.</p>
<p><a href="https://www.codecogs.com/eqnedit.php?latex=cov(X,Y)=\sum_{i=1}^{N}\frac{(X_{i}-\overline{X})(Y_{i}-\overline{Y})}{N}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?cov(X,Y)=\sum_{i=1}^{N}\frac{(X_{i}-\overline{X})(Y_{i}-\overline{Y})}{N}" title="cov(X,Y)=\sum_{i=1}^{N}\frac{(X_{i}-\overline{X})(Y_{i}-\overline{Y})}{N}" /></a><br />
où <a href="https://www.codecogs.com/eqnedit.php?latex=\overline{X}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\overline{X}" title="\overline{X}" /></a> , <a href="https://www.codecogs.com/eqnedit.php?latex=\overline{Y}" target="_blank"><img src="https://latex.codecogs.com/gif.latex?\overline{Y}" title="\overline{Y}" /></a> est la valeur attendue (moyenne) des variables X et Y respectivement. </p>
<p>covariance est symétrique donc cov(X,Y)=cov(Y,X).</p>
<p>La covariance est utile lorsque l'on regarde la variance de la somme des 2 variables aléatoires depuis : var(X+Y) = var(X) +var(Y) +2cov(X,Y). La covariance cov(x,y) est utilisée pour obtenir le coefficient de corrélation cor(x,y) en normalisant (divisant) cov(x,y) mais le produit des écarts types de x et y.</p>
term editor (obo:IAO_0000117)
en
Alejandra Gonzalez-Beltran
en
Philippe Rocca-Serra
en
Orlaith Burke
fr
Jean-Marc Meunier
definition source (obo:IAO_0000119)
<p>adapted from : <a href="http://mathworld.wolfram.com/Covariance.html" onclick="window.open(this.href, '', 'resizable=yes,status=no,location=no,toolbar=no,menubar=no,fullscreen=no,scrollbars=yes,dependent=no'); return false;">http://mathworld.wolfram.com/Covariance.html</a> and <a href="https://fr.wikipedia.org/wiki/Covariance" onclick="window.open(this.href, '', 'resizable=yes,status=no,location=no,toolbar=no,menubar=no,fullscreen=no,scrollbars=yes,dependent=no'); return false;">https://fr.wikipedia.org/wiki/Covariance</a></p>
STATO alternative term (obo:STATO_0000032)
en
Covariance
R command (obo:STATO_0000041)
<p>cov(x, y = NULL, use = "everything", method = c("pearson", "kendall", "spearman"))</p>
<p>from : <a href="http://stat.ethz.ch/R-manual/R-patched/library/stats/html/cor.html" onclick="window.open(this.href, '', 'resizable=yes,status=no,location=no,toolbar=no,menubar=no,fullscreen=no,scrollbars=yes,dependent=no'); return false;">http://stat.ethz.ch/R-manual/R-patched/library/stats/html/cor.html</a></p>
<p>from : <a href="http://stat.ethz.ch/R-manual/R-patched/library/stats/html/cor.html" onclick="window.open(this.href, '', 'resizable=yes,status=no,location=no,toolbar=no,menubar=no,fullscreen=no,scrollbars=yes,dependent=no'); return false;">http://stat.ethz.ch/R-manual/R-patched/library/stats/html/cor.html</a></p>
ISI Glossary (ont:ISI_Glossary)
<p>Traductions : <a href="http://isi.cbs.nl/glossary/term787.htm" onclick="window.open(this.href, '', 'resizable=yes,status=no,location=no,toolbar=no,menubar=no,fullscreen=no,scrollbars=yes,dependent=no'); return false;">http://isi.cbs.nl/glossary/term787.htm</a></p>
type (rdf:type)
subClassOf (rdfs:subClassOf)
has broader (skos:broader)
hierarchy level (md:hierarchyLevel)
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Filtre par propriété
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Coefficient de corrélation | Class |
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Structure de covariance | Class |
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Fiche concept statistique | Dataset |
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Fiche concept statistique | Dataset |
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Fiche concept statistique | Dataset |
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Donnée de mesure | Class |
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