mean += (value - mean) / (index + 1);
      } else {
        mean = calculateNewMeanNonFinite(mean, value);
      }
    }
    return mean;
  }

  /**
   * Returns the <a href="http://en.wikipedia.org/wiki/Arithmetic_mean">arithmetic mean</a> of the
   * values. The count must be non-zero.
   *
   * <p>The definition of the mean is the same as {@link Stats#mean}.
   *

   1. Definitions.

      "License" shall mean the terms and conditions for use, reproduction,
      and distribution as defined by Sections 1 through 9 of this document.

      "Licensor" shall mean the copyright owner or entity authorized by
      the copyright owner that is granting the License.

      "Legal Entity" shall mean the union of the acting entity and all

   1. Definitions.

      "License" shall mean the terms and conditions for use, reproduction,
      and distribution as defined by Sections 1 through 9 of this document.

      "Licensor" shall mean the copyright owner or entity authorized by
      the copyright owner that is granting the License.

      "Legal Entity" shall mean the union of the acting entity and all

TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION

1. Definitions.

"License" shall mean the terms and conditions for use, reproduction, and
distribution as defined by Sections 1 through 9 of this document.

"Licensor" shall mean the copyright owner or entity authorized by the copyright
owner that is granting the License.

"Legal Entity" shall mean the union of the acting entity and all other entities

      }
    } else {
      count++;
      if (isFinite(value) && isFinite(mean)) {
        // Art of Computer Programming vol. 2, Knuth, 4.2.2, (15) and (16)
        double delta = value - mean;
        mean += delta / count;
        sumOfSquaresOfDeltas += delta * (value - mean);
      } else {
        mean = calculateNewMeanNonFinite(mean, value);
        sumOfSquaresOfDeltas = NaN;
      }
      min = Math.min(min, value);

        return LinearTransformation.mapping(xStats.mean(), yStats.mean())
            .withSlope(sumOfProductsOfDeltas / xSumOfSquaresOfDeltas);
      } else {
        return LinearTransformation.horizontal(yStats.mean());
      }
    } else {
      checkState(yStats.sumOfSquaresOfDeltas() > 0.0);
      return LinearTransformation.vertical(xStats.mean());
    }
  }

  /**
   * {@inheritDoc}
   *

        for (int i = 1; i < values.length; i++) {
          mean = mean + (values[i] - mean) / (i + 1);
        }
        return mean;
      }
    };

    abstract double mean(double[] values);
  }

  static class MeanAndVariance {
    private final double mean;
    private final double variance;

    MeanAndVariance(double mean, double variance) {
      this.mean = mean;
      this.variance = variance;
    }

  public void testMedian_compute_doubleCollection() {
    assertThat(median().compute(SIXTEEN_SQUARES_DOUBLES))
        .isWithin(ALLOWED_ERROR)
        .of(SIXTEEN_SQUARES_MEDIAN);
  }

  public void testMedian_computeInPlace() {
    double[] dataset = Doubles.toArray(SIXTEEN_SQUARES_DOUBLES);
    assertThat(median().computeInPlace(dataset)).isWithin(ALLOWED_ERROR).of(SIXTEEN_SQUARES_MEDIAN);

  public void testMedian_compute_doubleCollection() {
    assertThat(median().compute(SIXTEEN_SQUARES_DOUBLES))
        .isWithin(ALLOWED_ERROR)
        .of(SIXTEEN_SQUARES_MEDIAN);
  }

  public void testMedian_computeInPlace() {
    double[] dataset = Doubles.toArray(SIXTEEN_SQUARES_DOUBLES);
    assertThat(median().computeInPlace(dataset)).isWithin(ALLOWED_ERROR).of(SIXTEEN_SQUARES_MEDIAN);

        twoValuesAccumulator.xStats().mean(),
        twoValuesAccumulator.yStats().mean(),
        twoValuesAccumulator.xStats().populationVariance(),
        twoValuesAccumulator.populationCovariance());
    assertDiagonalLinearTransformation(
        twoValuesAccumulatorByAddAllPartitionedPairedStats.leastSquaresFit(),
        twoValuesAccumulatorByAddAllPartitionedPairedStats.xStats().mean(),