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\rho_P(x,y) = \frac{{\rm cov}(x,y)}{\sigma(x) \sigma(y)} = \frac{ \sum\limits^n_{i=1} (x_i - \bar x)( y_i - \bar y)}{\sqrt{\sum\limits_i (x_i - \bar x)} \cdot \sqrt{\sum\limits_i (y_i - \bar y)}} |
where
where
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body | --uriencoded--\displaystyle \bar x = \frac%7B1%7D%7Bn%7D \sum\limits_%7Bi=1%7D%5en x_i |
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body | --uriencoded--\displaystyle \bar y = \frac%7B1%7D%7Bn%7D \sum\limits_%7Bi=1%7D%5en y_i |
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body | x=\{ x_1, \, x_2, \, ... x_n \} |
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and LaTeX Math Inline |
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body | y=\{ y_1, \, y_2, \, ... y_n \} |
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| finite arrays of -variable and -variable values |
| covariance between -variable and -variable |
| standard deviation of -variable and -variable |
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Fig. 1. Highly correlated variables | Fig. 2. Poorly correlated variables | Fig. 3. Highly anti-correlated variables |
See also
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Formal science / Mathematics / Statistics / Statistical correlation
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