*
   * <p>This fit minimizes the root-mean-square error in {@code y} as a function of {@code x}. This
   * error is defined as the square root of the mean of the squares of the differences between the
   * actual {@code y} values of the data and the values predicted by the fit for the {@code x}

   *
   * <p>This fit minimizes the root-mean-square error in {@code y} as a function of {@code x}. This
   * error is defined as the square root of the mean of the squares of the differences between the
   * actual {@code y} values of the data and the values predicted by the fit for the {@code x}

   *
   * <p>This fit minimizes the root-mean-square error in {@code y} as a function of {@code x}. This
   * error is defined as the square root of the mean of the squares of the differences between the
   * actual {@code y} values of the data and the values predicted by the fit for the {@code x}

   *
   * <p>This fit minimizes the root-mean-square error in {@code y} as a function of {@code x}. This
   * error is defined as the square root of the mean of the squares of the differences between the
   * actual {@code y} values of the data and the values predicted by the fit for the {@code x}

      LinearTransformation fit = accumulator.leastSquaresFit();
      LinearTransformation fitByAddAllPartitionedPairedStats =
          accumulatorByAddAllPartitionedPairedStats.leastSquaresFit();
      if (values.hasAnyNonFinite()) {
        assertLinearTransformationNaN(fit);
        assertLinearTransformationNaN(fitByAddAllPartitionedPairedStats);
      } else {

      PairedStats stats = createPairedStatsOf(values.asIterable(), OTHER_MANY_VALUES);
      LinearTransformation fit = stats.leastSquaresFit();
      if (values.hasAnyNonFinite()) {
        assertLinearTransformationNaN(fit);
      } else {
        assertDiagonalLinearTransformation(
            fit,
            stats.xStats().mean(),
            stats.yStats().mean(),
            stats.xStats().populationVariance(),

      LinearTransformation fit = accumulator.leastSquaresFit();
      LinearTransformation fitByAddAllPartitionedPairedStats =
          accumulatorByAddAllPartitionedPairedStats.leastSquaresFit();
      if (values.hasAnyNonFinite()) {
        assertLinearTransformationNaN(fit);
        assertLinearTransformationNaN(fitByAddAllPartitionedPairedStats);
      } else {

   * @return a single array containing all the values from the source arrays, in order
   * @throws IllegalArgumentException if the total number of elements in {@code arrays} does not fit
   *     in an {@code int}
   */
  public static boolean[] concat(boolean[]... arrays) {
    long length = 0;
    for (boolean[] array : arrays) {
      length += array.length;
    }

   * @return a single array containing all the values from the source arrays, in order
   * @throws IllegalArgumentException if the total number of elements in {@code arrays} does not fit
   *     in an {@code int}
   */
  public static boolean[] concat(boolean[]... arrays) {
    long length = 0;
    for (boolean[] array : arrays) {
      length += array.length;
    }

   * @param arrays zero or more {@code byte} arrays
   * @return a single array containing all the values from the source arrays, in order
   * @throws IllegalArgumentException if the total number of elements in {@code arrays} does not fit
   *     in an {@code int}
   */
  public static byte[] concat(byte[]... arrays) {
    long length = 0;
    for (byte[] array : arrays) {
      length += array.length;
    }