Trend unit tests (#1899)

- Move TREND() functions into the Statistical Trends class
- Unit tests for TREND()
- Create Confidence class for Statistical Confidence functions

src/PhpSpreadsheet/Calculation/Statistical/Trends.php

@@ -226,7 +226,7 @@ class Trends
                 ],
                 [
                     $bestFitLinear->getSlopeSE(),
-                    $bestFitLinear->getIntersectSE(),
+                    ($const === false) ? Functions::NA() : $bestFitLinear->getIntersectSE(),
                 ],
                 [
                     $bestFitLinear->getGoodnessOfFit(),
@@ -293,7 +293,7 @@ class Trends
                 ],
                 [
                     $bestFitExponential->getSlopeSE(),
-                    $bestFitExponential->getIntersectSE(),
+                    ($const === false) ? Functions::NA() : $bestFitExponential->getIntersectSE(),
                 ],
                 [
                     $bestFitExponential->getGoodnessOfFit(),

src/PhpSpreadsheet/Shared/Trend/BestFit.php

@@ -348,13 +348,13 @@ class BestFit
             $bestFitY = $this->yBestFitValues[$xKey] = $this->getValueOfYForX($xValue);
 
             $SSres += ($this->yValues[$xKey] - $bestFitY) * ($this->yValues[$xKey] - $bestFitY);
-            if ($const) {
+            if ($const === true) {
                 $SStot += ($this->yValues[$xKey] - $meanY) * ($this->yValues[$xKey] - $meanY);
             } else {
                 $SStot += $this->yValues[$xKey] * $this->yValues[$xKey];
             }
             $SScov += ($this->xValues[$xKey] - $meanX) * ($this->yValues[$xKey] - $meanY);
-            if ($const) {
+            if ($const === true) {
                 $SSsex += ($this->xValues[$xKey] - $meanX) * ($this->xValues[$xKey] - $meanX);
             } else {
                 $SSsex += $this->xValues[$xKey] * $this->xValues[$xKey];
@@ -362,7 +362,7 @@ class BestFit
         }
 
         $this->SSResiduals = $SSres;
-        $this->DFResiduals = $this->valueCount - 1 - $const;
+        $this->DFResiduals = $this->valueCount - 1 - ($const === true ? 1 : 0);
 
         if ($this->DFResiduals == 0.0) {
             $this->stdevOfResiduals = 0.0;
@@ -395,27 +395,39 @@ class BestFit
         }
     }
 
+    private function sumSquares(array $values)
+    {
+        return array_sum(
+            array_map(
+                function ($value) {
+                    return $value ** 2;
+                },
+                $values
+            )
+        );
+    }
+
     /**
      * @param float[] $yValues
      * @param float[] $xValues
-     * @param bool $const
      */
-    protected function leastSquareFit(array $yValues, array $xValues, $const): void
+    protected function leastSquareFit(array $yValues, array $xValues, bool $const): void
     {
         // calculate sums
-        $x_sum = array_sum($xValues);
+        $sumValuesX = array_sum($xValues);
-        $y_sum = array_sum($yValues);
+        $sumValuesY = array_sum($yValues);
-        $meanX = $x_sum / $this->valueCount;
+        $meanValueX = $sumValuesX / $this->valueCount;
-        $meanY = $y_sum / $this->valueCount;
+        $meanValueY = $sumValuesY / $this->valueCount;
-        $mBase = $mDivisor = $xx_sum = $xy_sum = $yy_sum = 0.0;
+        $sumSquaresX = $this->sumSquares($xValues);
+        $sumSquaresY = $this->sumSquares($yValues);
+        $mBase = $mDivisor = 0.0;
+        $xy_sum = 0.0;
         for ($i = 0; $i < $this->valueCount; ++$i) {
             $xy_sum += $xValues[$i] * $yValues[$i];
-            $xx_sum += $xValues[$i] * $xValues[$i];
-            $yy_sum += $yValues[$i] * $yValues[$i];
 
-            if ($const) {
+            if ($const === true) {
-                $mBase += ($xValues[$i] - $meanX) * ($yValues[$i] - $meanY);
+                $mBase += ($xValues[$i] - $meanValueX) * ($yValues[$i] - $meanValueY);
-                $mDivisor += ($xValues[$i] - $meanX) * ($xValues[$i] - $meanX);
+                $mDivisor += ($xValues[$i] - $meanValueX) * ($xValues[$i] - $meanValueX);
             } else {
                 $mBase += $xValues[$i] * $yValues[$i];
                 $mDivisor += $xValues[$i] * $xValues[$i];
@@ -426,13 +438,9 @@ class BestFit
         $this->slope = $mBase / $mDivisor;
 
         // calculate intersect
-        if ($const) {
+        $this->intersect = ($const === true) ? $meanValueY - ($this->slope * $meanValueX) : 0.0;
-            $this->intersect = $meanY - ($this->slope * $meanX);
-        } else {
-            $this->intersect = 0;
-        }
 
-        $this->calculateGoodnessOfFit($x_sum, $y_sum, $xx_sum, $yy_sum, $xy_sum, $meanX, $meanY, $const);
+        $this->calculateGoodnessOfFit($sumValuesX, $sumValuesY, $sumSquaresX, $sumSquaresY, $xy_sum, $meanValueX, $meanValueY, $const);
     }
 
     /**
@@ -440,23 +448,22 @@ class BestFit
      *
      * @param float[] $yValues The set of Y-values for this regression
      * @param float[] $xValues The set of X-values for this regression
-     * @param bool $const
      */
-    public function __construct($yValues, $xValues = [], $const = true)
+    public function __construct($yValues, $xValues = [])
     {
         //    Calculate number of points
-        $nY = count($yValues);
+        $yValueCount = count($yValues);
-        $nX = count($xValues);
+        $xValueCount = count($xValues);
 
         //    Define X Values if necessary
-        if ($nX == 0) {
+        if ($xValueCount === 0) {
-            $xValues = range(1, $nY);
+            $xValues = range(1, $yValueCount);
-        } elseif ($nY != $nX) {
+        } elseif ($yValueCount !== $xValueCount) {
             //    Ensure both arrays of points are the same size
             $this->error = true;
         }
 
-        $this->valueCount = $nY;
+        $this->valueCount = $yValueCount;
         $this->xValues = $xValues;
         $this->yValues = $yValues;
     }

src/PhpSpreadsheet/Shared/Trend/ExponentialBestFit.php

@@ -88,20 +88,17 @@ class ExponentialBestFit extends BestFit
      *
      * @param float[] $yValues The set of Y-values for this regression
      * @param float[] $xValues The set of X-values for this regression
-     * @param bool $const
      */
-    private function exponentialRegression($yValues, $xValues, $const): void
+    private function exponentialRegression(array $yValues, array $xValues, bool $const): void
     {
-        foreach ($yValues as &$value) {
+        $adjustedYValues = array_map(
-            if ($value < 0.0) {
+            function ($value) {
-                $value = 0 - log(abs($value));
+                return ($value < 0.0) ? 0 - log(abs($value)) : log($value);
-            } elseif ($value > 0.0) {
+            },
-                $value = log($value);
+            $yValues
-            }
+        );
-        }
-        unset($value);
 
-        $this->leastSquareFit($yValues, $xValues, $const);
+        $this->leastSquareFit($adjustedYValues, $xValues, $const);
     }
 
     /**
@@ -116,7 +113,7 @@ class ExponentialBestFit extends BestFit
         parent::__construct($yValues, $xValues);
 
         if (!$this->error) {
-            $this->exponentialRegression($yValues, $xValues, $const);
+            $this->exponentialRegression($yValues, $xValues, (bool) $const);
         }
     }
 }

src/PhpSpreadsheet/Shared/Trend/LinearBestFit.php

@@ -56,9 +56,8 @@ class LinearBestFit extends BestFit
      *
      * @param float[] $yValues The set of Y-values for this regression
      * @param float[] $xValues The set of X-values for this regression
-     * @param bool $const
      */
-    private function linearRegression($yValues, $xValues, $const): void
+    private function linearRegression(array $yValues, array $xValues, bool $const): void
     {
         $this->leastSquareFit($yValues, $xValues, $const);
     }
@@ -75,7 +74,7 @@ class LinearBestFit extends BestFit
         parent::__construct($yValues, $xValues);
 
         if (!$this->error) {
-            $this->linearRegression($yValues, $xValues, $const);
+            $this->linearRegression($yValues, $xValues, (bool) $const);
         }
     }
 }

src/PhpSpreadsheet/Shared/Trend/LogarithmicBestFit.php

@@ -48,7 +48,7 @@ class LogarithmicBestFit extends BestFit
         $slope = $this->getSlope($dp);
         $intersect = $this->getIntersect($dp);
 
-        return 'Y = ' . $intersect . ' + ' . $slope . ' * log(X)';
+        return 'Y = ' . $slope . ' * log(' . $intersect . ' * X)';
     }
 
     /**
@@ -56,20 +56,17 @@ class LogarithmicBestFit extends BestFit
      *
      * @param float[] $yValues The set of Y-values for this regression
      * @param float[] $xValues The set of X-values for this regression
-     * @param bool $const
      */
-    private function logarithmicRegression($yValues, $xValues, $const): void
+    private function logarithmicRegression(array $yValues, array $xValues, bool $const): void
     {
-        foreach ($xValues as &$value) {
+        $adjustedYValues = array_map(
-            if ($value < 0.0) {
+            function ($value) {
-                $value = 0 - log(abs($value));
+                return ($value < 0.0) ? 0 - log(abs($value)) : log($value);
-            } elseif ($value > 0.0) {
+            },
-                $value = log($value);
+            $yValues
-            }
+        );
-        }
-        unset($value);
 
-        $this->leastSquareFit($yValues, $xValues, $const);
+        $this->leastSquareFit($adjustedYValues, $xValues, $const);
     }
 
     /**
@@ -84,7 +81,7 @@ class LogarithmicBestFit extends BestFit
         parent::__construct($yValues, $xValues);
 
         if (!$this->error) {
-            $this->logarithmicRegression($yValues, $xValues, $const);
+            $this->logarithmicRegression($yValues, $xValues, (bool) $const);
         }
     }
 }

src/PhpSpreadsheet/Shared/Trend/PolynomialBestFit.php

@@ -178,9 +178,8 @@ class PolynomialBestFit extends BestFit
      * @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 bool $const
      */
-    public function __construct($order, $yValues, $xValues = [], $const = true)
+    public function __construct($order, $yValues, $xValues = [])
     {
         parent::__construct($yValues, $xValues);
 

src/PhpSpreadsheet/Shared/Trend/PowerBestFit.php

@@ -72,28 +72,23 @@ class PowerBestFit extends BestFit
      *
      * @param float[] $yValues The set of Y-values for this regression
      * @param float[] $xValues The set of X-values for this regression
-     * @param bool $const
      */
-    private function powerRegression($yValues, $xValues, $const): void
+    private function powerRegression(array $yValues, array $xValues, bool $const): void
     {
-        foreach ($xValues as &$value) {
+        $adjustedYValues = array_map(
-            if ($value < 0.0) {
+            function ($value) {
-                $value = 0 - log(abs($value));
+                return ($value < 0.0) ? 0 - log(abs($value)) : log($value);
-            } elseif ($value > 0.0) {
+            },
-                $value = log($value);
+            $yValues
-            }
+        );
-        }
+        $adjustedXValues = array_map(
-        unset($value);
+            function ($value) {
-        foreach ($yValues as &$value) {
+                return ($value < 0.0) ? 0 - log(abs($value)) : log($value);
-            if ($value < 0.0) {
+            },
-                $value = 0 - log(abs($value));
+            $xValues
-            } elseif ($value > 0.0) {
+        );
-                $value = log($value);
-            }
-        }
-        unset($value);
 
-        $this->leastSquareFit($yValues, $xValues, $const);
+        $this->leastSquareFit($adjustedYValues, $adjustedXValues, $const);
     }
 
     /**
@@ -108,7 +103,7 @@ class PowerBestFit extends BestFit
         parent::__construct($yValues, $xValues);
 
         if (!$this->error) {
-            $this->powerRegression($yValues, $xValues, $const);
+            $this->powerRegression($yValues, $xValues, (bool) $const);
         }
     }
 }

src/PhpSpreadsheet/Shared/Trend/Trend.php

@@ -55,10 +55,9 @@ class Trend
         $nX = count($xValues);
 
         //    Define X Values if necessary
-        if ($nX == 0) {
+        if ($nX === 0) {
             $xValues = range(1, $nY);
-            $nX = $nY;
+        } elseif ($nY !== $nX) {
-        } elseif ($nY != $nX) {
             //    Ensure both arrays of points are the same size
             trigger_error('Trend(): Number of elements in coordinate arrays do not match.', E_USER_ERROR);
         }
@@ -84,7 +83,7 @@ class Trend
             case self::TREND_POLYNOMIAL_6:
                 if (!isset(self::$trendCache[$key])) {
                     $order = substr($trendType, -1);
-                    self::$trendCache[$key] = new PolynomialBestFit($order, $yValues, $xValues, $const);
+                    self::$trendCache[$key] = new PolynomialBestFit($order, $yValues, $xValues);
                 }
 
                 return self::$trendCache[$key];
@@ -100,7 +99,7 @@ class Trend
                 if ($trendType != self::TREND_BEST_FIT_NO_POLY) {
                     foreach (self::$trendTypePolynomialOrders as $trendMethod) {
                         $order = substr($trendMethod, -1);
-                        $bestFit[$trendMethod] = new PolynomialBestFit($order, $yValues, $xValues, $const);
+                        $bestFit[$trendMethod] = new PolynomialBestFit($order, $yValues, $xValues);
                         if ($bestFit[$trendMethod]->getError()) {
                             unset($bestFit[$trendMethod]);
                         } else {

tests/PhpSpreadsheetTests/Calculation/Functions/Statistical/LogEstTest.php

@@ -19,7 +19,7 @@ class LogEstTest extends TestCase
     public function testLOGEST($expectedResult, $yValues, $xValues, $const, $stats): void
     {
         $result = Statistical::LOGEST($yValues, $xValues, $const, $stats);
-
+        //var_dump($result);
         $elements = count($expectedResult);
         for ($element = 0; $element < $elements; ++$element) {
             self::assertEqualsWithDelta($expectedResult[$element], $result[$element], 1E-12);

tests/PhpSpreadsheetTests/Shared/Trend/ExponentialBestFitTest.php

@@ -0,0 +1,49 @@
+<?php
+
+namespace PhpOffice\PhpSpreadsheetTests\Shared\Trend;
+
+use PhpOffice\PhpSpreadsheet\Shared\Trend\ExponentialBestFit;
+use PHPUnit\Framework\TestCase;
+
+class ExponentialBestFitTest extends TestCase
+{
+    /**
+     * @dataProvider providerExponentialBestFit
+     *
+     * @param mixed $expectedSlope
+     * @param mixed $expectedIntersect
+     * @param mixed $expectedGoodnessOfFit
+     * @param mixed $yValues
+     * @param mixed $xValues
+     * @param mixed $expectedEquation
+     */
+    public function testExponentialBestFit(
+        $expectedSlope,
+        $expectedIntersect,
+        $expectedGoodnessOfFit,
+        $expectedEquation,
+        $yValues,
+        $xValues
+    ): void {
+        $bestFit = new ExponentialBestFit($yValues, $xValues);
+        $slope = $bestFit->getSlope(1);
+        self::assertEquals($expectedSlope[0], $slope);
+        $slope = $bestFit->getSlope();
+        self::assertEquals($expectedSlope[1], $slope);
+        $intersect = $bestFit->getIntersect(1);
+        self::assertEquals($expectedIntersect[0], $intersect);
+        $intersect = $bestFit->getIntersect();
+        self::assertEquals($expectedIntersect[1], $intersect);
+
+        $equation = $bestFit->getEquation(2);
+        self::assertEquals($expectedEquation, $equation);
+
+        self::assertSame($expectedGoodnessOfFit[0], $bestFit->getGoodnessOfFit(6));
+        self::assertSame($expectedGoodnessOfFit[1], $bestFit->getGoodnessOfFit());
+    }
+
+    public function providerExponentialBestFit()
+    {
+        return require 'tests/data/Shared/Trend/ExponentialBestFit.php';
+    }
+}

tests/PhpSpreadsheetTests/Shared/Trend/LinearBestFitTest.php

@@ -0,0 +1,49 @@
+<?php
+
+namespace PhpOffice\PhpSpreadsheetTests\Shared\Trend;
+
+use PhpOffice\PhpSpreadsheet\Shared\Trend\LinearBestFit;
+use PHPUnit\Framework\TestCase;
+
+class LinearBestFitTest extends TestCase
+{
+    /**
+     * @dataProvider providerLinearBestFit
+     *
+     * @param mixed $expectedSlope
+     * @param mixed $expectedIntersect
+     * @param mixed $expectedGoodnessOfFit
+     * @param mixed $yValues
+     * @param mixed $xValues
+     * @param mixed $expectedEquation
+     */
+    public function testLinearBestFit(
+        $expectedSlope,
+        $expectedIntersect,
+        $expectedGoodnessOfFit,
+        $expectedEquation,
+        $yValues,
+        $xValues
+    ): void {
+        $bestFit = new LinearBestFit($yValues, $xValues);
+        $slope = $bestFit->getSlope(1);
+        self::assertEquals($expectedSlope[0], $slope);
+        $slope = $bestFit->getSlope();
+        self::assertEquals($expectedSlope[1], $slope);
+        $intersect = $bestFit->getIntersect(1);
+        self::assertEquals($expectedIntersect[0], $intersect);
+        $intersect = $bestFit->getIntersect();
+        self::assertEquals($expectedIntersect[1], $intersect);
+
+        $equation = $bestFit->getEquation(2);
+        self::assertEquals($expectedEquation, $equation);
+
+        self::assertSame($expectedGoodnessOfFit[0], $bestFit->getGoodnessOfFit(6));
+        self::assertSame($expectedGoodnessOfFit[1], $bestFit->getGoodnessOfFit());
+    }
+
+    public function providerLinearBestFit()
+    {
+        return require 'tests/data/Shared/Trend/LinearBestFit.php';
+    }
+}

tests/data/Calculation/Statistical/LINEST.php

@@ -1,6 +1,22 @@
 <?php
 
+use PhpOffice\PhpSpreadsheet\Calculation\Functions;
+
 return [
+    [
+        [1.0, 0.0],
+        [1, 2, 3, 4, 5],
+        [1, 2, 3, 4, 5],
+        false,
+        false,
+    ],
+    [
+        [2.310344827586207, 0.0],
+        [1, 9, 5, 7],
+        [0, 4, 2, 3],
+        false,
+        false,
+    ],
     [
         [2.0, 1.0],
         [1, 9, 5, 7],
@@ -8,6 +24,46 @@ return [
         true,
         false,
     ],
+    [
+        [0.600378787879, 0.0],
+        [3, 10, 3, 6, 8, 12, 1, 4, 9, 14],
+        [8, 2, 11, 6, 5, 4, 12, 9, 6, 1],
+        false,
+        false,
+    ],
+    [
+        [-1.1064189189190, 14.081081081081],
+        [3, 10, 3, 6, 8, 12, 1, 4, 9, 14],
+        [8, 2, 11, 6, 5, 4, 12, 9, 6, 1],
+        true,
+        false,
+    ],
+    [
+        [
+            [0.600378787879, 0.0],
+            [0.3130441135917, Functions::NA()],
+            [0.2901220667036, 7.193206086629],
+            [3.6782360429317, 9],
+            [190.3200757575760, 465.679924242424],
+        ],
+        [3, 10, 3, 6, 8, 12, 1, 4, 9, 14],
+        [8, 2, 11, 6, 5, 4, 12, 9, 6, 1],
+        false,
+        true,
+    ],
+    [
+        [
+            [-1.1064189189190, 14.081081081081],
+            [0.1491074289251, 1.083468383961],
+            [0.8731378215565, 1.622464237727],
+            [55.0605598780781, 8],
+            [144.9408783783780, 21.059121621622],
+        ],
+        [3, 10, 3, 6, 8, 12, 1, 4, 9, 14],
+        [8, 2, 11, 6, 5, 4, 12, 9, 6, 1],
+        true,
+        true,
+    ],
     [
         [
             [56.837944664032, 11704.347826086974],
@@ -21,29 +77,29 @@ return [
         true,
         true,
     ],
-    //    [
+    //        'multi-series' => [
-    //        [
+    //            [
-    //            [-234.2371645, 2553.21066, 12529.76817, 27.64138737, 52317.83051],
+    //                [-234.2371645, 2553.21066, 12529.76817, 27.64138737, 52317.83051],
-    //            [13.26801148, 530.6691519, 400.0668382, 5.429374042, 12237.3616],
+    //                [13.26801148, 530.6691519, 400.0668382, 5.429374042, 12237.3616],
-    //            [0.996747993, 970.5784629, '#N/A', '#N/A', '#N/A'],
+    //                [0.996747993, 970.5784629, '#N/A', '#N/A', '#N/A'],
-    //            [459.7536742, 6, '#N/A', '#N/A', '#N/A'],
+    //                [459.7536742, 6, '#N/A', '#N/A', '#N/A'],
-    //            [1732393319, 5652135.316, '#N/A', '#N/A', '#N/A'],
+    //                [1732393319, 5652135.316, '#N/A', '#N/A', '#N/A'],
+    //            ],
+    //            [142000, 144000, 151000, 150000, 139000, 169000, 126000, 142900, 163000, 169000, 149000],
+    //            [
+    //                [2310, 2, 2, 20],
+    //                [2333, 2, 2, 12],
+    //                [2356, 3, 1.5, 33],
+    //                [2379, 3, 2, 43],
+    //                [2402, 2, 3, 53],
+    //                [2425, 4, 2, 23],
+    //                [2448, 2, 1.5, 99],
+    //                [2471, 2, 2, 34],
+    //                [2494, 3, 3, 23],
+    //                [2517, 4, 4, 55],
+    //                [2540, 2, 3, 22],
+    //            ],
+    //            true,
+    //            true,
     //        ],
-    //        [142000, 144000, 151000, 150000, 139000, 169000, 126000, 142900, 163000, 169000, 149000],
-    //        [
-    //            [2310, 2, 2, 20],
-    //            [2333, 2, 2, 12],
-    //            [2356, 3, 1.5, 33],
-    //            [2379, 3, 2, 43],
-    //            [2402, 2, 3, 53],
-    //            [2425, 4, 2, 23],
-    //            [2448, 2, 1.5, 99],
-    //            [2471, 2, 2, 34],
-    //            [2494, 3, 3, 23],
-    //            [2517, 4, 4, 55],
-    //            [2540, 2, 3, 22],
-    //        ],
-    //        true,
-    //        true,
-    //    ],
 ];

tests/data/Calculation/Statistical/LOGEST.php

@@ -1,6 +1,20 @@
 <?php
 
 return [
+    [
+        [1.000174230092, 1.0],
+        [1, 2, 3, 4, 5],
+        [1, 10, 100, 1000, 10000],
+        false,
+        false,
+    ],
+    [
+        [1.000091183030, 2.127357620703],
+        [1, 2, 3, 4, 5],
+        [1, 10, 100, 1000, 10000],
+        true,
+        false,
+    ],
     [
         [1.463275628116, 495.304770158727],
         [33100, 47300, 69000, 102000, 150000, 220000],
@@ -15,4 +29,44 @@ return [
         true,
         false,
     ],
+    [
+        [1.1743674215053, 1.0],
+        [3, 10, 3, 6, 8, 12, 1, 4, 9, 14],
+        [8, 2, 11, 6, 5, 4, 12, 9, 6, 1],
+        false,
+        false,
+    ],
+    [
+        [0.8135120728565, 20.671878197178],
+        [3, 10, 3, 6, 8, 12, 1, 4, 9, 14],
+        [8, 2, 11, 6, 5, 4, 12, 9, 6, 1],
+        true,
+        false,
+    ],
+    //    [
+    //        [
+    //            [1.174367421505266, 1.0],
+    //            [0.0672620306083, Functions::NA()],
+    //            [0.3881799938732, 1.545563794251],
+    //            [5.7102087376569, 9],
+    //            [13.6403607201119, 21.498906978904],
+    //        ],
+    //        [3, 10, 3, 6, 8, 12, 1, 4, 9, 14],
+    //        [8, 2, 11, 6, 5, 4, 12, 9, 6, 1],
+    //        false,
+    //        true,
+    //    ],
+    //    [
+    //        [
+    //            [0.8135120728565, 20.671878197178],
+    //            [0.0313021171611, 0.227452478657],
+    //            [0.8445875527654, 0.340603858743],
+    //            [43.4759283593386, 8],
+    //            [5.0436854288511, 0.928087908723],
+    //        ],
+    //        [3, 10, 3, 6, 8, 12, 1, 4, 9, 14],
+    //        [8, 2, 11, 6, 5, 4, 12, 9, 6, 1],
+    //        true,
+    //        true,
+    //    ],
 ];

tests/data/Shared/Trend/ExponentialBestFit.php

@@ -0,0 +1,12 @@
+<?php
+
+return [
+    [
+        'slope' => [0.8, 0.813512072856517],
+        'intersect' => [20.7, 20.671878197177865],
+        'goodnessOfFit' => [0.904868, 0.9048681877346413],
+        'equation' => 'Y = 20.67 * 0.81^X',
+        [3, 10, 3, 6, 8, 12, 1, 4, 9, 14],
+        [8, 2, 11, 6, 5, 4, 12, 9, 6, 1],
+    ],
+];

tests/data/Shared/Trend/LinearBestFit.php

@@ -0,0 +1,20 @@
+<?php
+
+return [
+    [
+        'slope' => [-1.1, -1.1064189189190],
+        'intersect' => [14.1, 14.081081081081],
+        'goodnessOfFit' => [0.873138, 0.8731378215564962],
+        'equation' => 'Y = 14.08 + -1.11 * X',
+        [3, 10, 3, 6, 8, 12, 1, 4, 9, 14],
+        [8, 2, 11, 6, 5, 4, 12, 9, 6, 1],
+    ],
+    [
+        'slope' => [1.0, 1.0],
+        'intersect' => [-2.0, -2.0],
+        'goodnessOfFit' => [1.0, 1.0],
+        'equation' => 'Y = -2 + 1 * X',
+        [1, 2, 3, 4, 5],
+        [3, 4, 5, 6, 7],
+    ],
+];