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diff --git a/Marlin/src/libs/least_squares_fit.h b/Marlin/src/libs/least_squares_fit.h
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+/**
+ * Marlin 3D Printer Firmware
+ * Copyright (c) 2020 MarlinFirmware [https://github.com/MarlinFirmware/Marlin]
+ *
+ * Based on Sprinter and grbl.
+ * Copyright (c) 2011 Camiel Gubbels / Erik van der Zalm
+ *
+ * This program is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * This program is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with this program.  If not, see <https://www.gnu.org/licenses/>.
+ *
+ */
+#pragma once
+
+/**
+ * Incremental Least Squares Best Fit  By Roxy and Ed Williams
+ *
+ * This algorithm is high speed and has a very small code footprint.
+ * Its results are identical to both the Iterative Least-Squares published
+ * earlier by Roxy and the QR_SOLVE solution. If used in place of QR_SOLVE
+ * it saves roughly 10K of program memory.   And even better...  the data
+ * fed into the algorithm does not need to all be present at the same time.
+ * A point can be probed and its values fed into the algorithm and then discarded.
+ */
+
+#include "../inc/MarlinConfig.h"
+#include <math.h>
+
+struct linear_fit_data {
+  float xbar, ybar, zbar,
+        x2bar, y2bar, z2bar,
+        xybar, xzbar, yzbar,
+        max_absx, max_absy,
+        A, B, D, N;
+};
+
+inline void incremental_LSF_reset(struct linear_fit_data *lsf) {
+  memset(lsf, 0, sizeof(linear_fit_data));
+}
+
+inline void incremental_WLSF(struct linear_fit_data *lsf, const float &x, const float &y, const float &z, const float &w) {
+  // weight each accumulator by factor w, including the "number" of samples
+  // (analogous to calling inc_LSF twice with same values to weight it by 2X)
+  const float wx = w * x, wy = w * y, wz = w * z;
+  lsf->xbar  += wx;
+  lsf->ybar  += wy;
+  lsf->zbar  += wz;
+  lsf->x2bar += wx * x;
+  lsf->y2bar += wy * y;
+  lsf->z2bar += wz * z;
+  lsf->xybar += wx * y;
+  lsf->xzbar += wx * z;
+  lsf->yzbar += wy * z;
+  lsf->N     += w;
+  lsf->max_absx = _MAX(ABS(wx), lsf->max_absx);
+  lsf->max_absy = _MAX(ABS(wy), lsf->max_absy);
+}
+inline void incremental_WLSF(struct linear_fit_data *lsf, const xy_pos_t &pos, const float &z, const float &w) {
+  incremental_WLSF(lsf, pos.x, pos.y, z, w);
+}
+
+inline void incremental_LSF(struct linear_fit_data *lsf, const float &x, const float &y, const float &z) {
+  lsf->xbar += x;
+  lsf->ybar += y;
+  lsf->zbar += z;
+  lsf->x2bar += sq(x);
+  lsf->y2bar += sq(y);
+  lsf->z2bar += sq(z);
+  lsf->xybar += x * y;
+  lsf->xzbar += x * z;
+  lsf->yzbar += y * z;
+  lsf->max_absx = _MAX(ABS(x), lsf->max_absx);
+  lsf->max_absy = _MAX(ABS(y), lsf->max_absy);
+  lsf->N += 1.0;
+}
+inline void incremental_LSF(struct linear_fit_data *lsf, const xy_pos_t &pos, const float &z) {
+  incremental_LSF(lsf, pos.x, pos.y, z);
+}
+
+int finish_incremental_LSF(struct linear_fit_data *);