version=${projectVersion}...

        assertWithMessage("Pearson's correlation coefficient of " + values)
            .that(pearsonsCorrelationCoefficient)
            .isNaN();
        assertWithMessage("Pearson's correlation coefficient by addAll(PairedStats) of " + values)
            .that(pearsonsCorrelationCoefficient)
            .isNaN();
      } else {
        assertWithMessage("Pearson's correlation coefficient of " + values)

        assertWithMessage("Pearson's correlation coefficient of " + values)
            .that(pearsonsCorrelationCoefficient)
            .isNaN();
        assertWithMessage("Pearson's correlation coefficient by addAll(PairedStats) of " + values)
            .that(pearsonsCorrelationCoefficient)
            .isNaN();
      } else {
        assertWithMessage("Pearson's correlation coefficient of " + values)

    public void setFrom(String from, String personal) {
        doSetFrom(from, personal);
    }

    public void addTo(String to) {
        doAddTo(to);
    }

    public void addTo(String to, String personal) {
        doAddTo(to, personal);
    }

    public void addCc(String cc) {
        doAddCc(cc);
    }

    public void addCc(String cc, String personal) {
        doAddCc(cc, personal);
    }

John Howard <******@****.***> 1712111106 -0700

  }

  @Override
  public final ScheduledFuture<?> scheduleAtFixedRate(
      Runnable command, long initialDelay, long period, TimeUnit unit) {
    return delegate.scheduleAtFixedRate(wrapTask(command), initialDelay, period, unit);
  }

  @Override
  public final ScheduledFuture<?> scheduleWithFixedDelay(
      Runnable command, long initialDelay, long delay, TimeUnit unit) {

  public double sampleCovariance() {
    checkState(count() > 1);
    return sumOfProductsOfDeltas / (count() - 1);
  }

  /**
   * Returns the <a href="http://mathworld.wolfram.com/CorrelationCoefficient.html">Pearson's or
   * 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

    for (int i = 0; i < 8; i++) {
      limiter.acquire(); // // #1
    }
    stopwatch.sleepMillis(4500); // #2: back to cold state (warmup period + repay last acquire)
    for (int i = 0; i < 3; i++) { // only three steps, we're somewhere in the warmup period
      limiter.acquire(); // #3
    }

    limiter.setRate(4.0); // double the rate!

    ```Python hl_lines="26-33"
    {!> ../../../docs_src/path_operation_advanced_configuration/tutorial007_pv1.py!}
    ```

!!! info
    In Pydantic Version 1 war die Methode zum Parsen und Validieren eines Objekts `Item.parse_obj()`, in Pydantic Version 2 heißt die Methode `Item.model_validate()`.

!!! tip "Tipp"
    Hier verwenden wir dasselbe Pydantic-Modell wieder.

      if (values.hasAnyNonFinite()) {
        assertWithMessage("Pearson's correlation coefficient of " + values)
            .that(pearsonsCorrelationCoefficient)
            .isNaN();
      } else {
        assertWithMessage("Pearson's correlation coefficient of " + values)
            .that(pearsonsCorrelationCoefficient)
            .isWithin(ALLOWED_ERROR)