assertThrows(IllegalStateException.class, () -> EMPTY_STATS_VARARGS.populationVariance());
    assertThrows(IllegalStateException.class, () -> EMPTY_STATS_ITERABLE.populationVariance());
    assertThat(ONE_VALUE_STATS.populationVariance()).isEqualTo(0.0);
    assertThat(Stats.of(POSITIVE_INFINITY).populationVariance()).isNaN();
    assertThat(Stats.of(NEGATIVE_INFINITY).populationVariance()).isNaN();
    assertThat(Stats.of(NaN).populationVariance()).isNaN();

    assertThrows(IllegalStateException.class, () -> emptyAccumulator.populationVariance());
    assertThrows(
        IllegalStateException.class,
        () -> emptyAccumulatorByAddAllEmptyIterable.populationVariance());
    assertThrows(
        IllegalStateException.class, () -> emptyAccumulatorByAddAllEmptyStats.populationVariance());
    assertThat(oneValueAccumulator.populationVariance()).isEqualTo(0.0);

    assertThrows(IllegalStateException.class, () -> emptyAccumulator.populationVariance());
    assertThrows(
        IllegalStateException.class,
        () -> emptyAccumulatorByAddAllEmptyIterable.populationVariance());
    assertThrows(
        IllegalStateException.class, () -> emptyAccumulatorByAddAllEmptyStats.populationVariance());
    assertThat(oneValueAccumulator.populationVariance()).isEqualTo(0.0);

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

   * product-moment correlation coefficient</a> of the values. The count must greater than one, and
   * the {@code x} and {@code y} values must both have non-zero population variance (i.e. {@code
   * xStats().populationVariance() > 0.0 && yStats().populationVariance() > 0.0}). The result is not
   * guaranteed to be exactly +/-1 even when the data are perfectly (anti-)correlated, due to
   * numerical errors. However, it is guaranteed to be in the inclusive range [-1, +1].

   * product-moment correlation coefficient</a> of the values. The count must greater than one, and
   * the {@code x} and {@code y} values must both have non-zero population variance (i.e. {@code
   * xStats().populationVariance() > 0.0 && yStats().populationVariance() > 0.0}). The result is not
   * guaranteed to be exactly +/-1 even when the data are perfectly (anti-)correlated, due to
   * numerical errors. However, it is guaranteed to be in the inclusive range [-1, +1].

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

   * product-moment correlation coefficient</a> of the values. The count must greater than one, and
   * the {@code x} and {@code y} values must both have non-zero population variance (i.e. {@code
   * xStats().populationVariance() > 0.0 && yStats().populationVariance() > 0.0}). The result is not
   * guaranteed to be exactly +/-1 even when the data are perfectly (anti-)correlated, due to
   * numerical errors. However, it is guaranteed to be in the inclusive range [-1, +1].

   * product-moment correlation coefficient</a> of the values. The count must greater than one, and
   * the {@code x} and {@code y} values must both have non-zero population variance (i.e. {@code
   * xStats().populationVariance() > 0.0 && yStats().populationVariance() > 0.0}). The result is not
   * guaranteed to be exactly +/-1 even when the data are perfectly (anti-)correlated, due to
   * numerical errors. However, it is guaranteed to be in the inclusive range [-1, +1].

        TWO_VALUES_PAIRED_STATS.leastSquaresFit(),
        TWO_VALUES_PAIRED_STATS.xStats().mean(),
        TWO_VALUES_PAIRED_STATS.yStats().mean(),
        TWO_VALUES_PAIRED_STATS.xStats().populationVariance(),
        TWO_VALUES_PAIRED_STATS.populationCovariance());
    // For datasets of many double values, we test many combinations of finite and non-finite
    // x-values:
    for (ManyValues values : ALL_MANY_VALUES) {