diff --git a/src/PhpSpreadsheet/Shared/trend/polynomialBestFit.php b/src/PhpSpreadsheet/Shared/trend/polynomialBestFit.php
deleted file mode 100644
index 0c2c5894..00000000
--- a/src/PhpSpreadsheet/Shared/trend/polynomialBestFit.php
+++ /dev/null
@@ -1,221 +0,0 @@
-<?php
-
-namespace PHPExcel\Shared\trend;
-
-/**
- * PHPExcel_Polynomial_Best_Fit
- *
- * Copyright (c) 2006 - 2015 PHPExcel
- *
- * This library is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Lesser General Public
- * License as published by the Free Software Foundation; either
- * version 2.1 of the License, or (at your option) any later version.
- *
- * This library 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
- * Lesser General Public License for more details.
- *
- * You should have received a copy of the GNU Lesser General Public
- * License along with this library; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301  USA
- *
- * @category   PHPExcel
- * @package    PHPExcel_Shared_Trend
- * @copyright  Copyright (c) 2006 - 2015 PHPExcel (http://www.codeplex.com/PHPExcel)
- * @license    http://www.gnu.org/licenses/old-licenses/lgpl-2.1.txt    LGPL
- * @version    ##VERSION##, ##DATE##
- */
-class PolynomialBestFit extends BestFit
-{
-    /**
-     * Algorithm type to use for best-fit
-     * (Name of this trend class)
-     *
-     * @var    string
-     **/
-    protected $bestFitType = 'polynomial';
-
-    /**
-     * Polynomial order
-     *
-     * @protected
-     * @var    int
-     **/
-    protected $order = 0;
-
-
-    /**
-     * Return the order of this polynomial
-     *
-     * @return     int
-     **/
-    public function getOrder()
-    {
-        return $this->order;
-    }
-
-
-    /**
-     * Return the Y-Value for a specified value of X
-     *
-     * @param     float        $xValue            X-Value
-     * @return     float                        Y-Value
-     **/
-    public function getValueOfYForX($xValue)
-    {
-        $retVal = $this->getIntersect();
-        $slope = $this->getSlope();
-        foreach ($slope as $key => $value) {
-            if ($value != 0.0) {
-                $retVal += $value * pow($xValue, $key + 1);
-            }
-        }
-        return $retVal;
-    }
-
-
-    /**
-     * Return the X-Value for a specified value of Y
-     *
-     * @param     float        $yValue            Y-Value
-     * @return     float                        X-Value
-     **/
-    public function getValueOfXForY($yValue)
-    {
-        return ($yValue - $this->getIntersect()) / $this->getSlope();
-    }
-
-
-    /**
-     * Return the Equation of the best-fit line
-     *
-     * @param     int        $dp        Number of places of decimal precision to display
-     * @return     string
-     **/
-    public function getEquation($dp = 0)
-    {
-        $slope = $this->getSlope($dp);
-        $intersect = $this->getIntersect($dp);
-
-        $equation = 'Y = ' . $intersect;
-        foreach ($slope as $key => $value) {
-            if ($value != 0.0) {
-                $equation .= ' + ' . $value . ' * X';
-                if ($key > 0) {
-                    $equation .= '^' . ($key + 1);
-                }
-            }
-        }
-        return $equation;
-    }
-
-
-    /**
-     * Return the Slope of the line
-     *
-     * @param     int        $dp        Number of places of decimal precision to display
-     * @return     string
-     **/
-    public function getSlope($dp = 0)
-    {
-        if ($dp != 0) {
-            $coefficients = array();
-            foreach ($this->_slope as $coefficient) {
-                $coefficients[] = round($coefficient, $dp);
-            }
-            return $coefficients;
-        }
-        return $this->_slope;
-    }
-
-
-    public function getCoefficients($dp = 0)
-    {
-        return array_merge(array($this->getIntersect($dp)), $this->getSlope($dp));
-    }
-
-
-    /**
-     * Execute the regression and calculate the goodness of fit for a set of X and Y data values
-     *
-     * @param    int            $order        Order of Polynomial for this regression
-     * @param    float[]        $yValues    The set of Y-values for this regression
-     * @param    float[]        $xValues    The set of X-values for this regression
-     * @param    boolean        $const
-     */
-    private function polynomialRegression($order, $yValues, $xValues, $const)
-    {
-        // calculate sums
-        $x_sum = array_sum($xValues);
-        $y_sum = array_sum($yValues);
-        $xx_sum = $xy_sum = 0;
-        for ($i = 0; $i < $this->valueCount; ++$i) {
-            $xy_sum += $xValues[$i] * $yValues[$i];
-            $xx_sum += $xValues[$i] * $xValues[$i];
-            $yy_sum += $yValues[$i] * $yValues[$i];
-        }
-        /*
-         *    This routine uses logic from the PHP port of polyfit version 0.1
-         *    written by Michael Bommarito and Paul Meagher
-         *
-         *    The function fits a polynomial function of order $order through
-         *    a series of x-y data points using least squares.
-         *
-         */
-        for ($i = 0; $i < $this->valueCount; ++$i) {
-            for ($j = 0; $j <= $order; ++$j) {
-                $A[$i][$j] = pow($xValues[$i], $j);
-            }
-        }
-        for ($i=0; $i < $this->valueCount; ++$i) {
-            $B[$i] = array($yValues[$i]);
-        }
-        $matrixA = new Matrix($A);
-        $matrixB = new Matrix($B);
-        $C = $matrixA->solve($matrixB);
-
-        $coefficients = array();
-        for ($i = 0; $i < $C->m; ++$i) {
-            $r = $C->get($i, 0);
-            if (abs($r) <= pow(10, -9)) {
-                $r = 0;
-            }
-            $coefficients[] = $r;
-        }
-
-        $this->intersect = array_shift($coefficients);
-        $this->_slope = $coefficients;
-
-        $this->calculateGoodnessOfFit($x_sum, $y_sum, $xx_sum, $yy_sum, $xy_sum);
-        foreach ($this->xValues as $xKey => $xValue) {
-            $this->yBestFitValues[$xKey] = $this->getValueOfYForX($xValue);
-        }
-    }
-
-
-    /**
-     * Define the regression and calculate the goodness of fit for a set of X and Y data values
-     *
-     * @param    int            $order        Order of Polynomial for this regression
-     * @param    float[]        $yValues    The set of Y-values for this regression
-     * @param    float[]        $xValues    The set of X-values for this regression
-     * @param    boolean        $const
-     */
-    public function __construct($order, $yValues, $xValues = array(), $const = true)
-    {
-        if (parent::__construct($yValues, $xValues) !== false) {
-            if ($order < $this->valueCount) {
-                $this->bestFitType .= '_'.$order;
-                $this->order = $order;
-                $this->polynomialRegression($order, $yValues, $xValues, $const);
-                if (($this->getGoodnessOfFit() < 0.0) || ($this->getGoodnessOfFit() > 1.0)) {
-                    $this->_error = true;
-                }
-            } else {
-                $this->_error = true;
-            }
-        }
-    }
-}