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For the variance, we subtract each x and y value from its mean (columns B and E) and square these differences (columns C and F). The sums of the these squared deviations are shown in the last row of columns C and F. For the regression, we need the covariance, which requires that we take the difference between each x value and its mean and multiply it by the difference between each y value and its mean [(xi — X)(yi — y), column G] and then sum these products (last row of column G).

The variance is the sum of the squared deviations from the mean divided by n — 1, where n is the number of measurements:

The regression coefficient is the covariance divided by the variance of x:

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