for (ManyValues values : ALL_MANY_VALUES) {
      PairedStatsAccumulator accumulator =
          createFilledPairedStatsAccumulator(values.asIterable(), OTHER_MANY_VALUES);
      PairedStatsAccumulator accumulatorByAddAllPartitionedPairedStats =
          createPartitionedFilledPairedStatsAccumulator(values.asIterable(), OTHER_MANY_VALUES, 2);
      double populationCovariance = accumulator.populationCovariance();

    for (ManyValues values : ALL_MANY_VALUES) {
      PairedStatsAccumulator accumulator =
          createFilledPairedStatsAccumulator(values.asIterable(), OTHER_MANY_VALUES);
      PairedStatsAccumulator accumulatorByAddAllPartitionedPairedStats =
          createPartitionedFilledPairedStatsAccumulator(values.asIterable(), OTHER_MANY_VALUES, 2);
      double populationCovariance = accumulator.populationCovariance();

      StatsAccumulator accumulator = new StatsAccumulator();
      StatsAccumulator accumulatorByAddAllStats = new StatsAccumulator();
      accumulator.addAll(values.asIterable());
      for (double value : values.asIterable()) {
        accumulatorByAddAllStats.addAll(Stats.of(value));
      }
      double mean = accumulator.mean();
      double meanByAddAllStats = accumulatorByAddAllStats.mean();

      StatsAccumulator accumulator = new StatsAccumulator();
      StatsAccumulator accumulatorByAddAllStats = new StatsAccumulator();
      accumulator.addAll(values.asIterable());
      for (double value : values.asIterable()) {
        accumulatorByAddAllStats.addAll(Stats.of(value));
      }
      double mean = accumulator.mean();
      double meanByAddAllStats = accumulatorByAddAllStats.mean();

    // For datasets of many double values, we test many combinations of finite and non-finite
    // x-values:
    for (ManyValues values : ALL_MANY_VALUES) {
      PairedStats stats = createPairedStatsOf(values.asIterable(), OTHER_MANY_VALUES);
      double populationCovariance = stats.populationCovariance();
      if (values.hasAnyNonFinite()) {
        assertWithMessage("population covariance of " + values).that(populationCovariance).isNaN();

    // finite and non-finite values:
    for (ManyValues values : ALL_MANY_VALUES) {
      double populationVariance = Stats.of(values.asIterable()).populationVariance();
      if (values.hasAnyNonFinite()) {
        assertWithMessage("population variance of " + values).that(populationVariance).isNaN();
      } else {

Lkotlin/sequences/Sequence;
Lkotlin/sequences/SequencesKt;
Lkotlin/sequences/SequencesKt__SequencesKt$asSequence$$inlined$Sequence$1;
Lkotlin/sequences/SequencesKt___SequencesJvmKt;
Lkotlin/sequences/SequencesKt___SequencesKt$asIterable$$inlined$Iterable$1;
Lkotlin/sequences/TransformingSequence$iterator$1;
Lkotlin/sequences/TransformingSequence;
Lkotlin/text/CharsKt__CharKt;
Lkotlin/text/Charsets;
Lkotlin/text/DelimitedRangesSequence;

   * returns a {@link FluentIterable} of elements of an anonymous type whose supertypes are the <a
   * href="https://docs.oracle.com/javase/specs/jls/se7/html/jls-4.html#jls-4.9">intersection</a> of
   * the supertypes of {@code A} and the supertypes of {@code B}.
   */
  public void testConcatIntersectionType() {
    Iterable<A> aIterable = ImmutableList.of();