.of(TWO_VALUES_SUM_OF_PRODUCTS_OF_DELTAS / 2);
    assertThat(twoValuesAccumulatorByAddAllPartitionedPairedStats.populationCovariance())
        .isWithin(ALLOWED_ERROR)
        .of(TWO_VALUES_SUM_OF_PRODUCTS_OF_DELTAS / 2);
    assertThat(manyValuesAccumulator.populationCovariance())
        .isWithin(ALLOWED_ERROR)
        .of(MANY_VALUES_SUM_OF_PRODUCTS_OF_DELTAS / MANY_VALUES_COUNT);

        .of(TWO_VALUES_SUM_OF_PRODUCTS_OF_DELTAS / 2);
    assertThat(twoValuesAccumulatorByAddAllPartitionedPairedStats.populationCovariance())
        .isWithin(ALLOWED_ERROR)
        .of(TWO_VALUES_SUM_OF_PRODUCTS_OF_DELTAS / 2);
    assertThat(manyValuesAccumulator.populationCovariance())
        .isWithin(ALLOWED_ERROR)
        .of(MANY_VALUES_SUM_OF_PRODUCTS_OF_DELTAS / MANY_VALUES_COUNT);

    assertThat(TWO_VALUES_PAIRED_STATS.sampleCovariance())
        .isWithin(ALLOWED_ERROR)
        .of(TWO_VALUES_SUM_OF_PRODUCTS_OF_DELTAS);
    assertThat(MANY_VALUES_PAIRED_STATS.sampleCovariance())
        .isWithin(ALLOWED_ERROR)
        .of(MANY_VALUES_SUM_OF_PRODUCTS_OF_DELTAS / (MANY_VALUES_COUNT - 1));
    assertThat(HORIZONTAL_VALUES_PAIRED_STATS.sampleCovariance()).isWithin(ALLOWED_ERROR).of(0.0);

    assertThat(TWO_VALUES_PAIRED_STATS.sampleCovariance())
        .isWithin(ALLOWED_ERROR)
        .of(TWO_VALUES_SUM_OF_PRODUCTS_OF_DELTAS);
    assertThat(MANY_VALUES_PAIRED_STATS.sampleCovariance())
        .isWithin(ALLOWED_ERROR)
        .of(MANY_VALUES_SUM_OF_PRODUCTS_OF_DELTAS / (MANY_VALUES_COUNT - 1));
    assertThat(HORIZONTAL_VALUES_PAIRED_STATS.sampleCovariance()).isWithin(ALLOWED_ERROR).of(0.0);

      // If it won't fit in a long, then we store off the intermediate product.
      if (normalizedBits + productBits >= Long.SIZE) {
        bignums.add(BigInteger.valueOf(product));
        product = 1;
        productBits = 0;
      }
      product *= normalizedNum;
      productBits = LongMath.log2(product, FLOOR) + 1;
    }
    // Check for leftovers.
    if (product > 1) {
      bignums.add(BigInteger.valueOf(product));
    }

  static final ImmutableList<Double> OTHER_TWO_VALUES = ImmutableList.of(123.456, -789.012);
  static final double OTHER_TWO_VALUES_MEAN = (123.456 - 789.012) / 2;
  static final double TWO_VALUES_SUM_OF_PRODUCTS_OF_DELTAS =
      (12.34 - TWO_VALUES_MEAN) * (123.456 - OTHER_TWO_VALUES_MEAN)
          + (-56.78 - TWO_VALUES_MEAN) * (-789.012 - OTHER_TWO_VALUES_MEAN);

  /**

     * href="../../../nbtcodes.html">Here</a> is
     * a fairly complete list of NetBIOS hex codes. Scope is not used but is
     * still functional in other NetBIOS products and so for completeness it has been
     * implemented. A <code>scope</code> of <code>null</code> or <code>""</code>
     * signifies no scope.
     * 

    // vol. 2, Knuth, 4.2.2, (16) to the two-variable case. We have two value series x_i and y_i.
    // We define the arithmetic means X_n = 1/n \sum_{i=1}^n x_i, and Y_n = 1/n \sum_{i=1}^n y_i.
    // We also define the sum of the products of the differences from the means
    //           C_n = \sum_{i=1}^n x_i y_i - n X_n Y_n
    // for all n >= 1. Then for all n > 1:
    //       C_{n-1} = \sum_{i=1}^{n-1} x_i y_i - (n-1) X_{n-1} Y_{n-1}

  static final ImmutableList<Double> OTHER_TWO_VALUES = ImmutableList.of(123.456, -789.012);
  static final double OTHER_TWO_VALUES_MEAN = (123.456 - 789.012) / 2;
  static final double TWO_VALUES_SUM_OF_PRODUCTS_OF_DELTAS =
      (12.34 - TWO_VALUES_MEAN) * (123.456 - OTHER_TWO_VALUES_MEAN)
          + (-56.78 - TWO_VALUES_MEAN) * (-789.012 - OTHER_TWO_VALUES_MEAN);

  /**

   * }
   * }</pre>
   *
   * <p>Note that if any input set is empty, the Cartesian product will also be empty. If no sets at
   * all are provided (an empty list), the resulting Cartesian product has one element, an empty
   * list (counter-intuitive, but mathematically consistent).
   *
   * <p><i>Performance notes:</i> while the cartesian product of sets of size {@code m, n, p} is a