function sampleCovariance(x, y) {
    // The two datasets must have the same length which must be more than 1
    if (x.length !== y.length) {
        throw new Error("sampleCovariance requires samples with equal lengths");
    }

    if (x.length < 2) {
        throw new Error(
            "sampleCovariance requires at least two data points in each sample"
        );
    }

    // determine the mean of each dataset so that we can judge each
    // value of the dataset fairly as the difference from the mean. this
    // way, if one dataset is [1, 2, 3] and [2, 3, 4], their covariance
    // does not suffer because of the difference in absolute values
    const xmean = mean(x);
    const ymean = mean(y);
    let sum = 0;

    // for each pair of values, the covariance increases when their
    // difference from the mean is associated - if both are well above
    // or if both are well below
    // the mean, the covariance increases significantly.
    for (let i = 0; i < x.length; i++) {
        sum += (x[i] - xmean) * (y[i] - ymean);
    }

    // this is Bessels' Correction: an adjustment made to sample statistics
    // that allows for the reduced degree of freedom entailed in calculating
    // values from samples rather than complete populations.
    const besselsCorrection = x.length - 1;

    // the covariance is weighted by the length of the datasets.
    return sum / besselsCorrection;
}
[Sample covariance](https://en.wikipedia.org/wiki/Sample_mean_and_covariance) of two datasets:
how much do the two datasets move together?
x and y are two datasets, represented as arrays of numbers.

@param {Array<number>} x a sample of two or more data points
@param {Array<number>} y a sample of two or more data points
@throws {Error} if x and y do not have equal lengths
@throws {Error} if x or y have length of one or less
@returns {number} sample covariance
@example
sampleCovariance([1, 2, 3, 4, 5, 6], [6, 5, 4, 3, 2, 1]); // => -3.5