},
        "norwegian_keywords": {

  }

  @GwtIncompatible // DoubleMath.mean
  @SuppressWarnings("deprecation") // test of deprecated method
  public void testMean_intVarargs() {
    assertThat(DoubleMath.mean(11, -22, 44, -88)).isWithin(1.0e-10).of(-13.75);
    assertThat(DoubleMath.mean(11)).isWithin(1.0e-10).of(11.0);
  }

  @GwtIncompatible // DoubleMath.mean
  @SuppressWarnings("deprecation") // test of deprecated method

      // in NaN naturally.
      sumOfProductsOfDeltas +=
          values.sumOfProductsOfDeltas()
              + (values.xStats().mean() - xStats.mean())
                  * (values.yStats().mean() - yStats.mean())
                  * values.count();
    }
    yStats.addAll(values.yStats());
  }

  /** Returns an immutable snapshot of the current statistics. */

        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}.
   *

      }
    } 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;
    }

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

/**
 * Provides a fluent API for calculating <a
 * href="http://en.wikipedia.org/wiki/Quantile">quantiles</a>.
 *
 * <h3>Examples</h3>
 *
 * <p>To compute the median:
 *
 * {@snippet :
 * double myMedian = median().compute(myDataset);
 * }
 *
 * where {@link #median()} has been statically imported.
 *
 * <p>To compute the 99th percentile:
 *
 * {@snippet :
 * double myPercentile99 = percentiles().index(99).compute(myDataset);

  private static void assertMean(int x, int y) {
    int expectedMean = computeMeanSafely(x, y);
    assertEquals(expectedMean, IntMath.mean(x, y));
    assertEquals(
        "The mean of x and y should equal the mean of y and x", expectedMean, IntMath.mean(y, x));
  }

  /**
   * Computes the mean in a way that is obvious and resilient to overflow by using BigInteger
   * arithmetic.
   */