diff --git a/src/PhpSpreadsheet/Shared/trend/exponentialBestFit.php b/src/PhpSpreadsheet/Shared/trend/exponentialBestFit.php
deleted file mode 100644
index be705165..00000000
--- a/src/PhpSpreadsheet/Shared/trend/exponentialBestFit.php
+++ /dev/null
@@ -1,138 +0,0 @@
-<?php
-
-namespace PHPExcel\Shared\trend;
-
-/**
- * PHPExcel_Exponential_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 ExponentialBestFit extends BestFit
-{
-    /**
-     * Algorithm type to use for best-fit
-     * (Name of this trend class)
-     *
-     * @var    string
-     **/
-    protected $bestFitType        = 'exponential';
-
-    /**
-     * Return the Y-Value for a specified value of X
-     *
-     * @param     float        $xValue            X-Value
-     * @return     float                        Y-Value
-     **/
-    public function getValueOfYForX($xValue)
-    {
-        return $this->getIntersect() * pow($this->getSlope(), ($xValue - $this->xOffset));
-    }
-
-    /**
-     * Return the X-Value for a specified value of Y
-     *
-     * @param     float        $yValue            Y-Value
-     * @return     float                        X-Value
-     **/
-    public function getValueOfXForY($yValue)
-    {
-        return log(($yValue + $this->yOffset) / $this->getIntersect()) / log($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);
-
-        return 'Y = ' . $intersect . ' * ' . $slope . '^X';
-    }
-
-    /**
-     * 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 ($dp != 0) {
-            return round(exp($this->_slope), $dp);
-        }
-        return exp($this->_slope);
-    }
-
-    /**
-     * Return the Value of X where it intersects Y = 0
-     *
-     * @param     int        $dp        Number of places of decimal precision to display
-     * @return     string
-     **/
-    public function getIntersect($dp = 0)
-    {
-        if ($dp != 0) {
-            return round(exp($this->intersect), $dp);
-        }
-        return exp($this->intersect);
-    }
-
-    /**
-     * Execute the regression and calculate the goodness of fit for a set of X and Y data values
-     *
-     * @param     float[]    $yValues    The set of Y-values for this regression
-     * @param     float[]    $xValues    The set of X-values for this regression
-     * @param     boolean    $const
-     */
-    private function exponentialRegression($yValues, $xValues, $const)
-    {
-        foreach ($yValues as &$value) {
-            if ($value < 0.0) {
-                $value = 0 - log(abs($value));
-            } elseif ($value > 0.0) {
-                $value = log($value);
-            }
-        }
-        unset($value);
-
-        $this->leastSquareFit($yValues, $xValues, $const);
-    }
-
-    /**
-     * Define the regression and calculate the goodness of fit for a set of X and Y data values
-     *
-     * @param    float[]        $yValues    The set of Y-values for this regression
-     * @param    float[]        $xValues    The set of X-values for this regression
-     * @param    boolean        $const
-     */
-    public function __construct($yValues, $xValues = array(), $const = true)
-    {
-        if (parent::__construct($yValues, $xValues) !== false) {
-            $this->exponentialRegression($yValues, $xValues, $const);
-        }
-    }
-}