assertThat(TWO_VALUES_PAIRED_STATS.populationCovariance())
        .isWithin(ALLOWED_ERROR)
        .of(TWO_VALUES_SUM_OF_PRODUCTS_OF_DELTAS / 2);
    // 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();

    assertThat(TWO_VALUES_PAIRED_STATS.populationCovariance())
        .isWithin(ALLOWED_ERROR)
        .of(TWO_VALUES_SUM_OF_PRODUCTS_OF_DELTAS / 2);
    // 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();

        .isWithin(ALLOWED_ERROR)
        .of(MANY_VALUES_SUM_OF_PRODUCTS_OF_DELTAS / MANY_VALUES_COUNT);
    // For datasets of many double values, we test many combinations of finite and non-finite
    // x-values:
    for (ManyValues values : ALL_MANY_VALUES) {
      PairedStatsAccumulator accumulator =
          createFilledPairedStatsAccumulator(values.asIterable(), OTHER_MANY_VALUES);

        .isWithin(ALLOWED_ERROR)
        .of(MANY_VALUES_MEAN);
    // For datasets of many double values created from an iterable, we test many combinations of
    // finite and non-finite values:
    for (ManyValues values : ALL_MANY_VALUES) {
      StatsAccumulator accumulator = new StatsAccumulator();
      StatsAccumulator accumulatorByAddAllStats = new StatsAccumulator();

   * finite. Call either {@link LinearTransformationBuilder#and} or {@link
   * LinearTransformationBuilder#withSlope} on the returned object to finish building the instance.
   */
  public static LinearTransformationBuilder mapping(double x1, double y1) {
    checkArgument(isFinite(x1) && isFinite(y1));
    return new LinearTransformationBuilder(x1, y1);
  }

  /**

  /**
   * Helper class for testing with non-finite values. {@link #ALL_MANY_VALUES} gives a number of
   * instances with many combinations of finite and non-finite values. All have {@link
   * #MANY_VALUES_COUNT} values. If all the values are finite then the mean is {@link
   * #MANY_VALUES_MEAN} and the sum-of-squares-of-deltas is {@link
   * #MANY_VALUES_SUM_OF_SQUARES_OF_DELTAS}. The smallest and largest finite values are always

  static long getSignificand(double d) {
    checkArgument(isFinite(d), "not a normal value");
    int exponent = getExponent(d);
    long bits = doubleToRawLongBits(d);
    bits &= SIGNIFICAND_MASK;
    return (exponent == MIN_EXPONENT - 1) ? bits << 1 : bits | IMPLICIT_BIT;
  }

  static boolean isFinite(double d) {
    return getExponent(d) <= MAX_EXPONENT;
  }

    } catch (UnsupportedOperationException expected) {
    }
  }

  /**
   * Verifies that an Iterator is unmodifiable.
   *
   * <p>This test only works with iterators that iterate over a finite set.
   */
  public static void assertIteratorIsUnmodifiable(Iterator<?> iterator) {
    while (iterator.hasNext()) {
      iterator.next();
      try {
        iterator.remove();

    } catch (UnsupportedOperationException expected) {
    }
  }

  /**
   * Verifies that an Iterator is unmodifiable.
   *
   * <p>This test only works with iterators that iterate over a finite set.
   */
  public static void assertIteratorIsUnmodifiable(Iterator<?> iterator) {
    while (iterator.hasNext()) {
      iterator.next();
      try {
        iterator.remove();

 * infinite value and itself, the infinite value is returned. If required to do a weighted average
 * between {@link Double#NEGATIVE_INFINITY NEGATIVE_INFINITY} and {@link Double#POSITIVE_INFINITY
 * POSITIVE_INFINITY}, {@link Double#NaN NaN} is returned (note that this will only happen if the
 * dataset contains no finite values).
 *
 * <h3>Performance</h3>
 *