}

  /**
   * Returns the <a href="http://en.wikipedia.org/wiki/Variance#Sample_variance">unbiased sample
   * variance</a> of the values. If this dataset is a sample drawn from a population, this is an
   * unbiased estimator of the population variance of the population. The count must be greater than
   * one.
   *
   * <p>This is not guaranteed to return zero when the dataset consists of the same value multiple

   * R*R)} of the population standard deviation of {@code y}, where {@code R} is the Pearson's
   * correlation coefficient (as given by {@link #pearsonsCorrelationCoefficient()}).
   *
   * <p>The corresponding root-mean-square error in {@code x} as a function of {@code y} is a
   * fraction {@code sqrt(1/(R*R) - 1)} of the population standard deviation of {@code x}. This fit

  }

  /**
   * Returns the <a href="http://en.wikipedia.org/wiki/Variance#Sample_variance">unbiased sample
   * variance</a> of the values. If this dataset is a sample drawn from a population, this is an
   * unbiased estimator of the population variance of the population. The count must be greater than
   * one.
   *
   * <p>This is not guaranteed to return zero when the dataset consists of the same value multiple

   * R*R)} of the population standard deviation of {@code y}, where {@code R} is the Pearson's
   * correlation coefficient (as given by {@link #pearsonsCorrelationCoefficient()}).
   *
   * <p>The corresponding root-mean-square error in {@code x} as a function of {@code y} is a
   * fraction {@code sqrt(1/(R*R) - 1)} of the population standard deviation of {@code x}. This fit

   * R*R)} of the population standard deviation of {@code y}, where {@code R} is the Pearson's
   * correlation coefficient (as given by {@link #pearsonsCorrelationCoefficient()}).
   *
   * <p>The corresponding root-mean-square error in {@code x} as a function of {@code y} is a
   * fraction {@code sqrt(1/(R*R) - 1)} of the population standard deviation of {@code x}. This fit

  /**
   * Returns the <a href="http://en.wikipedia.org/wiki/Arithmetic_mean">arithmetic mean</a> of
   * {@code values}.
   *
   * <p>If these values are a sample drawn from a population, this is also an unbiased estimator of
   * the arithmetic mean of the population.
   *
   * @param values a nonempty series of values
   * @throws IllegalArgumentException if {@code values} is empty or contains any non-finite value

  /**
   * Returns the <a href="http://en.wikipedia.org/wiki/Arithmetic_mean">arithmetic mean</a> of
   * {@code values}.
   *
   * <p>If these values are a sample drawn from a population, this is also an unbiased estimator of
   * the arithmetic mean of the population.
   *
   * @param values a nonempty series of values
   * @throws IllegalArgumentException if {@code values} is empty or contains any non-finite value

   * R*R)} of the population standard deviation of {@code y}, where {@code R} is the Pearson's
   * correlation coefficient (as given by {@link #pearsonsCorrelationCoefficient()}).
   *
   * <p>The corresponding root-mean-square error in {@code x} as a function of {@code y} is a
   * fraction {@code sqrt(1/(R*R) - 1)} of the population standard deviation of {@code x}. This fit

      if (values.hasAnyNonFinite()) {
        assertWithMessage("population covariance of " + values).that(populationCovariance).isNaN();
        assertWithMessage("population covariance by addAll(PairedStats) of " + values)
            .that(populationCovarianceByAddAllPartitionedPairedStats)
            .isNaN();
      } else {
        assertWithMessage("population covariance of " + values)
            .that(populationCovariance)

  }

  /**
   * Returns the <a href="http://en.wikipedia.org/wiki/Variance#Sample_variance">unbiased sample
   * variance</a> of the values. If this dataset is a sample drawn from a population, this is an
   * unbiased estimator of the population variance of the population. The count must be greater than
   * one.
   *
   * <p>This is not guaranteed to return zero when the dataset consists of the same value multiple