* xStats().populationVariance() > 0.0 || yStats().populationVariance() > 0.0}). The result is
   * guaranteed to be horizontal if there is variance in the {@code x} data but not the {@code y}
   * data, and vertical if there is variance in the {@code y} data but not the {@code x} data.
   *
   * <p>This fit minimizes the root-mean-square error in {@code y} as a function of {@code x}. This

import com.google.j2objc.annotations.WeakOuter;
import java.util.concurrent.Executor;
import java.util.concurrent.TimeUnit;
import java.util.concurrent.TimeoutException;

/**
 * Base class for services that do not need a thread while "running" but may need one during startup
 * and shutdown. Subclasses can implement {@link #startUp} and {@link #shutDown} methods, each which

import java.util.concurrent.Executor;
import java.util.concurrent.TimeUnit;
import java.util.concurrent.TimeoutException;
import java.util.logging.Level;

/**
 * Base class for services that can implement {@link #startUp}, {@link #run} and {@link #shutDown}
 * methods. This class uses a single thread to execute the service; consider {@link AbstractService}
 * if you would like to manage any threading manually.
 *

    double x = 1.2;
    LinearTransformation transformation = LinearTransformation.vertical(x);
    assertVerticalLinearTransformation(transformation, x);
  }

  public void testVertical_infiniteX() {
    assertThrows(
        IllegalArgumentException.class,
        () -> LinearTransformation.vertical(Double.NEGATIVE_INFINITY));
  }

  public void testVertical_nanX() {

    double x = 1.2;
    LinearTransformation transformation = LinearTransformation.vertical(x);
    assertVerticalLinearTransformation(transformation, x);
  }

  public void testVertical_infiniteX() {
    assertThrows(
        IllegalArgumentException.class,
        () -> LinearTransformation.vertical(Double.NEGATIVE_INFINITY));
  }

  public void testVertical_nanX() {

        HORIZONTAL_VALUES_PAIRED_STATS.leastSquaresFit(),
        HORIZONTAL_VALUES_PAIRED_STATS.yStats().mean());
    assertVerticalLinearTransformation(
        VERTICAL_VALUES_PAIRED_STATS.leastSquaresFit(),
        VERTICAL_VALUES_PAIRED_STATS.xStats().mean());
    assertThrows(IllegalStateException.class, () -> CONSTANT_VALUES_PAIRED_STATS.leastSquaresFit());
  }

  public void testEqualsAndHashCode() {

        HORIZONTAL_VALUES_PAIRED_STATS.leastSquaresFit(),
        HORIZONTAL_VALUES_PAIRED_STATS.yStats().mean());
    assertVerticalLinearTransformation(
        VERTICAL_VALUES_PAIRED_STATS.leastSquaresFit(),
        VERTICAL_VALUES_PAIRED_STATS.xStats().mean());
    assertThrows(IllegalStateException.class, () -> CONSTANT_VALUES_PAIRED_STATS.leastSquaresFit());
  }

  public void testEqualsAndHashCode() {

   */
  public static final byte LF = 10;

  /**
   * Alternate name for {@link #LF}. ({@code LF} is preferred.)
   *
   * @since 8.0
   */
  public static final byte NL = 10;

  /**
   * Vertical Tabulation ('\v'): A format effector which controls the movement of the printing
   * position to the next in a series of predetermined printing lines. (Applicable also to display
   * devices.)
   *
   * @since 8.0

   */
  public void testTransitionRace() throws TimeoutException {
    for (int k = 0; k < 1000; k++) {
      List<Service> services = Lists.newArrayList();
      for (int i = 0; i < 5; i++) {
        services.add(new SnappyShutdownService(i));
      }
      ServiceManager manager = new ServiceManager(services);
      manager.startAsync().awaitHealthy();
      manager.stopAsync().awaitStopped(10, TimeUnit.SECONDS);
    }
  }

   * xStats().populationVariance() > 0.0 || yStats().populationVariance() > 0.0}). The result is
   * guaranteed to be horizontal if there is variance in the {@code x} data but not the {@code y}
   * data, and vertical if there is variance in the {@code y} data but not the {@code x} data.
   *
   * <p>This fit minimizes the root-mean-square error in {@code y} as a function of {@code x}. This