Pearson 相关系数, 计算X和Y之间的线性相关程度,范围[-1, +1]。+1表示正线性相关,0表示线性无关,-1表示负线性相关。
公式:
}{\sigma_X \sigma_Y})
其中, = E[(X-\mu_X)(Y-\mu_Y)])
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^2] = E[X^2] - (E[X])^2)
^2] = E[Y^2] - (E[Y])^2)
 = E[(X-\mu_X)(Y-\mu_Y)] = E[XY]-E[X]E[Y])
 = \frac{\sum_{i=1}^n{(X_i - \bar X)(Y_i - \bar Y)}}{n-1})
Cov: 协方差
σ: 标准差
Sample PCC
(y_i - \bar y)}}{\sqrt{\sum_{i=1}^n{(x_i - \bar x)2}}\sqrt{\sum_{i=1}n{(y_i - \bar y)^2}}})