Trends::COVAR() - Code Metrics - Inspection of "Merge pull request #3373 from PHPOffice/CalcEngine..." - PHPOffice/PhpSpreadsheet - Measure and Improve Code Quality continuously with Scrutinizer

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Push — master ( 9e1378...d02904 )

by Mark

created 2023-02-17 05:43 UTC

Trends::COVAR() A

↳ Parent: Trends

Complexity

Conditions	2
Paths	3

Size

Total Lines	12
Code Lines	7

Duplication

Lines	0
Ratio	0 %

Code Coverage

Tests	7
CRAP Score	2

Importance

Changes

Metric	Value
cc	2
eloc	7
nc	3
nop	2
dl	0
loc	12
rs	10
c	0
b	0
f	0
ccs	7
cts	7
cp	1
crap	2

<?php

namespace PhpOffice\PhpSpreadsheet\Calculation\Statistical;

use PhpOffice\PhpSpreadsheet\Calculation\ArrayEnabled;
use PhpOffice\PhpSpreadsheet\Calculation\Exception;
use PhpOffice\PhpSpreadsheet\Calculation\Functions;
use PhpOffice\PhpSpreadsheet\Calculation\Information\ExcelError;
use PhpOffice\PhpSpreadsheet\Shared\Trend\Trend;

class Trends
{
    use ArrayEnabled;

    private static function filterTrendValues(array &$array1, array &$array2): void
    {
        foreach ($array1 as $key => $value) {
            if ((is_bool($value)) || (is_string($value)) || ($value === null)) {
                unset($array1[$key], $array2[$key]);
            }
        }
    }

    /**
     * @param mixed $array1 should be array, but scalar is made into one
     * @param mixed $array2 should be array, but scalar is made into one
     */
    private static function checkTrendArrays(&$array1, &$array2): void
    {
        if (!is_array($array1)) {
            $array1 = [$array1];
        }
        if (!is_array($array2)) {
            $array2 = [$array2];
        }

        $array1 = Functions::flattenArray($array1);
        $array2 = Functions::flattenArray($array2);

        self::filterTrendValues($array1, $array2);
        self::filterTrendValues($array2, $array1);

        // Reset the array indexes
        $array1 = array_merge($array1);
        $array2 = array_merge($array2);
    }

    protected static function validateTrendArrays(array $yValues, array $xValues): void
    {
        $yValueCount = count($yValues);
        $xValueCount = count($xValues);

        if (($yValueCount === 0) || ($yValueCount !== $xValueCount)) {
            throw new Exception(ExcelError::NA());
        } elseif ($yValueCount === 1) {
            throw new Exception(ExcelError::DIV0());
        }
    }

    /**
     * CORREL.
     *
     * Returns covariance, the average of the products of deviations for each data point pair.
     *
     * @param mixed $yValues array of mixed Data Series Y
     * @param null|mixed $xValues array of mixed Data Series X
     *
     * @return float|string
     */
    public static function CORREL($yValues, $xValues = null)
    {
        if (($xValues === null) || (!is_array($yValues)) || (!is_array($xValues))) {
            return ExcelError::VALUE();
        }

        try {
            self::checkTrendArrays($yValues, $xValues);
            self::validateTrendArrays($yValues, $xValues);
        } catch (Exception $e) {
            return $e->getMessage();
        }

        $bestFitLinear = Trend::calculate(Trend::TREND_LINEAR, $yValues, $xValues);

        return $bestFitLinear->getCorrelation();
    }

    /**
     * COVAR.
     *
     * Returns covariance, the average of the products of deviations for each data point pair.
     *
     * @param mixed $yValues array of mixed Data Series Y
     * @param mixed $xValues array of mixed Data Series X
     *
     * @return float|string
     */
    public static function COVAR($yValues, $xValues)
    {
        try {
            self::checkTrendArrays($yValues, $xValues);
            self::validateTrendArrays($yValues, $xValues);
        } catch (Exception $e) {
            return $e->getMessage();
        }

        $bestFitLinear = Trend::calculate(Trend::TREND_LINEAR, $yValues, $xValues);

        return $bestFitLinear->getCovariance();
    }

    /**
     * FORECAST.
     *
     * Calculates, or predicts, a future value by using existing values.
     * The predicted value is a y-value for a given x-value.
     *
     * @param mixed $xValue Float value of X for which we want to find Y
     *                      Or can be an array of values
     * @param mixed $yValues array of mixed Data Series Y
     * @param mixed $xValues of mixed Data Series X
     *
     * @return array|bool|float|string
     *         If an array of numbers is passed as an argument, then the returned result will also be an array
     *            with the same dimensions
     */
    public static function FORECAST($xValue, $yValues, $xValues)
    {
        if (is_array($xValue)) {
            return self::evaluateArrayArgumentsSubset([self::class, __FUNCTION__], 1, $xValue, $yValues, $xValues);
        }

        try {
            $xValue = StatisticalValidations::validateFloat($xValue);
            self::checkTrendArrays($yValues, $xValues);
            self::validateTrendArrays($yValues, $xValues);
        } catch (Exception $e) {
            return $e->getMessage();
        }

        $bestFitLinear = Trend::calculate(Trend::TREND_LINEAR, $yValues, $xValues);

        return $bestFitLinear->getValueOfYForX($xValue);
    }

    /**
     * GROWTH.
     *
     * Returns values along a predicted exponential Trend
     *
     * @param mixed[] $yValues Data Series Y
     * @param mixed[] $xValues Data Series X
     * @param mixed[] $newValues Values of X for which we want to find Y
     * @param mixed $const A logical (boolean) value specifying whether to force the intersect to equal 0 or not
     *
     * @return float[]
     */
    public static function GROWTH($yValues, $xValues = [], $newValues = [], $const = true)
    {
        $yValues = Functions::flattenArray($yValues);
        $xValues = Functions::flattenArray($xValues);
        $newValues = Functions::flattenArray($newValues);
        $const = ($const === null) ? true : (bool) Functions::flattenSingleValue($const);

        $bestFitExponential = Trend::calculate(Trend::TREND_EXPONENTIAL, $yValues, $xValues, $const);
        if (empty($newValues)) {
            $newValues = $bestFitExponential->getXValues();
        }

        $returnArray = [];
        foreach ($newValues as $xValue) {
            $returnArray[0][] = [$bestFitExponential->getValueOfYForX($xValue)];
        }

        return $returnArray; //* @phpstan-ignore-line
    }

    /**
     * INTERCEPT.
     *
     * Calculates the point at which a line will intersect the y-axis by using existing x-values and y-values.
     *
     * @param mixed[] $yValues Data Series Y
     * @param mixed[] $xValues Data Series X
     *
     * @return float|string
     */
    public static function INTERCEPT($yValues, $xValues)
    {
        try {
            self::checkTrendArrays($yValues, $xValues);
            self::validateTrendArrays($yValues, $xValues);
        } catch (Exception $e) {
            return $e->getMessage();
        }

        $bestFitLinear = Trend::calculate(Trend::TREND_LINEAR, $yValues, $xValues);

        return $bestFitLinear->getIntersect();
    }

    /**
     * LINEST.
     *
     * Calculates the statistics for a line by using the "least squares" method to calculate a straight line
     *     that best fits your data, and then returns an array that describes the line.
     *
     * @param mixed[] $yValues Data Series Y
     * @param null|mixed[] $xValues Data Series X
     * @param mixed $const A logical (boolean) value specifying whether to force the intersect to equal 0 or not
     * @param mixed $stats A logical (boolean) value specifying whether to return additional regression statistics
     *
     * @return array|int|string The result, or a string containing an error
     */
    public static function LINEST($yValues, $xValues = null, $const = true, $stats = false)
    {
        $const = ($const === null) ? true : (bool) Functions::flattenSingleValue($const);
        $stats = ($stats === null) ? false : (bool) Functions::flattenSingleValue($stats);
        if ($xValues === null) {
            $xValues = $yValues;
        }

        try {
            self::checkTrendArrays($yValues, $xValues);
            self::validateTrendArrays($yValues, $xValues);
        } catch (Exception $e) {
            return $e->getMessage();
        }

        $bestFitLinear = Trend::calculate(Trend::TREND_LINEAR, $yValues, $xValues, $const);

        if ($stats === true) {
            return [
                [
                    $bestFitLinear->getSlope(),
                    $bestFitLinear->getIntersect(),
                ],
                [
                    $bestFitLinear->getSlopeSE(),
                    ($const === false) ? ExcelError::NA() : $bestFitLinear->getIntersectSE(),
                ],
                [
                    $bestFitLinear->getGoodnessOfFit(),
                    $bestFitLinear->getStdevOfResiduals(),
                ],
                [
                    $bestFitLinear->getF(),
                    $bestFitLinear->getDFResiduals(),
                ],
                [
                    $bestFitLinear->getSSRegression(),
                    $bestFitLinear->getSSResiduals(),
                ],
            ];
        }

        return [
            $bestFitLinear->getSlope(),
            $bestFitLinear->getIntersect(),
        ];
    }

    /**
     * LOGEST.
     *
     * Calculates an exponential curve that best fits the X and Y data series,
     *        and then returns an array that describes the line.
     *
     * @param mixed[] $yValues Data Series Y
     * @param null|mixed[] $xValues Data Series X
     * @param mixed $const A logical (boolean) value specifying whether to force the intersect to equal 0 or not
     * @param mixed $stats A logical (boolean) value specifying whether to return additional regression statistics
     *
     * @return array|int|string The result, or a string containing an error
     */
    public static function LOGEST($yValues, $xValues = null, $const = true, $stats = false)
    {
        $const = ($const === null) ? true : (bool) Functions::flattenSingleValue($const);
        $stats = ($stats === null) ? false : (bool) Functions::flattenSingleValue($stats);
        if ($xValues === null) {
            $xValues = $yValues;
        }

        try {
            self::checkTrendArrays($yValues, $xValues);
            self::validateTrendArrays($yValues, $xValues);
        } catch (Exception $e) {
            return $e->getMessage();
        }

        foreach ($yValues as $value) {
            if ($value < 0.0) {
                return ExcelError::NAN();
            }
        }

        $bestFitExponential = Trend::calculate(Trend::TREND_EXPONENTIAL, $yValues, $xValues, $const);

        if ($stats === true) {
            return [
                [
                    $bestFitExponential->getSlope(),
                    $bestFitExponential->getIntersect(),
                ],
                [
                    $bestFitExponential->getSlopeSE(),
                    ($const === false) ? ExcelError::NA() : $bestFitExponential->getIntersectSE(),
                ],
                [
                    $bestFitExponential->getGoodnessOfFit(),
                    $bestFitExponential->getStdevOfResiduals(),
                ],
                [
                    $bestFitExponential->getF(),
                    $bestFitExponential->getDFResiduals(),
                ],
                [
                    $bestFitExponential->getSSRegression(),
                    $bestFitExponential->getSSResiduals(),
                ],
            ];
        }

        return [
            $bestFitExponential->getSlope(),
            $bestFitExponential->getIntersect(),
        ];
    }

    /**
     * RSQ.
     *
     * Returns the square of the Pearson product moment correlation coefficient through data points
     *     in known_y's and known_x's.
     *
     * @param mixed[] $yValues Data Series Y
     * @param mixed[] $xValues Data Series X
     *
     * @return float|string The result, or a string containing an error
     */
    public static function RSQ($yValues, $xValues)
    {
        try {
            self::checkTrendArrays($yValues, $xValues);
            self::validateTrendArrays($yValues, $xValues);
        } catch (Exception $e) {
            return $e->getMessage();
        }

        $bestFitLinear = Trend::calculate(Trend::TREND_LINEAR, $yValues, $xValues);

        return $bestFitLinear->getGoodnessOfFit();
    }

    /**
     * SLOPE.
     *
     * Returns the slope of the linear regression line through data points in known_y's and known_x's.
     *
     * @param mixed[] $yValues Data Series Y
     * @param mixed[] $xValues Data Series X
     *
     * @return float|string The result, or a string containing an error
     */
    public static function SLOPE($yValues, $xValues)
    {
        try {
            self::checkTrendArrays($yValues, $xValues);
            self::validateTrendArrays($yValues, $xValues);
        } catch (Exception $e) {
            return $e->getMessage();
        }

        $bestFitLinear = Trend::calculate(Trend::TREND_LINEAR, $yValues, $xValues);

        return $bestFitLinear->getSlope();
    }

    /**
     * STEYX.
     *
     * Returns the standard error of the predicted y-value for each x in the regression.
     *
     * @param mixed[] $yValues Data Series Y
     * @param mixed[] $xValues Data Series X
     *
     * @return float|string
     */
    public static function STEYX($yValues, $xValues)
    {
        try {
            self::checkTrendArrays($yValues, $xValues);
            self::validateTrendArrays($yValues, $xValues);
        } catch (Exception $e) {
            return $e->getMessage();
        }

        $bestFitLinear = Trend::calculate(Trend::TREND_LINEAR, $yValues, $xValues);

        return $bestFitLinear->getStdevOfResiduals();
    }

    /**
     * TREND.
     *
     * Returns values along a linear Trend
     *
     * @param mixed[] $yValues Data Series Y
     * @param mixed[] $xValues Data Series X
     * @param mixed[] $newValues Values of X for which we want to find Y
     * @param mixed $const A logical (boolean) value specifying whether to force the intersect to equal 0 or not
     *
     * @return float[]
     */
    public static function TREND($yValues, $xValues = [], $newValues = [], $const = true)
    {
        $yValues = Functions::flattenArray($yValues);
        $xValues = Functions::flattenArray($xValues);
        $newValues = Functions::flattenArray($newValues);
        $const = ($const === null) ? true : (bool) Functions::flattenSingleValue($const);

        $bestFitLinear = Trend::calculate(Trend::TREND_LINEAR, $yValues, $xValues, $const);
        if (empty($newValues)) {
            $newValues = $bestFitLinear->getXValues();
        }

        $returnArray = [];
        foreach ($newValues as $xValue) {
            $returnArray[0][] = [$bestFitLinear->getValueOfYForX($xValue)];
        }

        return $returnArray; //* @phpstan-ignore-line
    }
}


1		<?php
2
3		namespace PhpOffice\PhpSpreadsheet\Calculation\Statistical;
4
5		use PhpOffice\PhpSpreadsheet\Calculation\ArrayEnabled;
6		use PhpOffice\PhpSpreadsheet\Calculation\Exception;
7		use PhpOffice\PhpSpreadsheet\Calculation\Functions;
8		use PhpOffice\PhpSpreadsheet\Calculation\Information\ExcelError;
9		use PhpOffice\PhpSpreadsheet\Shared\Trend\Trend;
10
11		class Trends
12		{
13		use ArrayEnabled;
14
15	60	private static function filterTrendValues(array &$array1, array &$array2): void
16		{
17	60	foreach ($array1 as $key => $value) {
18	58	if ((is_bool($value)) \|\| (is_string($value)) \|\| ($value === null)) {
19	5	unset($array1[$key], $array2[$key]);
20		}
21		}
22		}
23
24		/**
25		* @param mixed $array1 should be array, but scalar is made into one
26		* @param mixed $array2 should be array, but scalar is made into one
27		*/
28	60	private static function checkTrendArrays(&$array1, &$array2): void
29		{
30	60	if (!is_array($array1)) {
31		$array1 = [$array1];
32		}
33	60	if (!is_array($array2)) {
34		$array2 = [$array2];
35		}
36
37	60	$array1 = Functions::flattenArray($array1);
38	60	$array2 = Functions::flattenArray($array2);
39
40	60	self::filterTrendValues($array1, $array2);
41	60	self::filterTrendValues($array2, $array1);
42
43		// Reset the array indexes
44	60	$array1 = array_merge($array1);
45	60	$array2 = array_merge($array2);
46		}
47
48	60	protected static function validateTrendArrays(array $yValues, array $xValues): void
49		{
50	60	$yValueCount = count($yValues);
51	60	$xValueCount = count($xValues);
52
53	60	if (($yValueCount === 0) \|\| ($yValueCount !== $xValueCount)) {
54	7	throw new Exception(ExcelError::NA());
55	53	} elseif ($yValueCount === 1) {
56	7	throw new Exception(ExcelError::DIV0());
57		}
58		}
59
60		/**
61		* CORREL.
62		*
63		* Returns covariance, the average of the products of deviations for each data point pair.
64		*
65		* @param mixed $yValues array of mixed Data Series Y
66		* @param null\|mixed $xValues array of mixed Data Series X
67		*
68		* @return float\|string
69		*/
70	7	public static function CORREL($yValues, $xValues = null)
71		{
72	7	if (($xValues === null) \|\| (!is_array($yValues)) \|\| (!is_array($xValues))) {
73	1	return ExcelError::VALUE();
74		}
75
76		try {
77	6	self::checkTrendArrays($yValues, $xValues);
78	6	self::validateTrendArrays($yValues, $xValues);
79	2	} catch (Exception $e) {
80	2	return $e->getMessage();
81		}
82
83	4	$bestFitLinear = Trend::calculate(Trend::TREND_LINEAR, $yValues, $xValues);
84
85	4	return $bestFitLinear->getCorrelation();
86		}
87
88		/**
89		* COVAR.
90		*
91		* Returns covariance, the average of the products of deviations for each data point pair.
92		*
93		* @param mixed $yValues array of mixed Data Series Y
94		* @param mixed $xValues array of mixed Data Series X
95		*
96		* @return float\|string
97		*/
98	7	public static function COVAR($yValues, $xValues)
99		{
100		try {
101	7	self::checkTrendArrays($yValues, $xValues);
102	7	self::validateTrendArrays($yValues, $xValues);
103	2	} catch (Exception $e) {
104	2	return $e->getMessage();
105		}
106
107	5	$bestFitLinear = Trend::calculate(Trend::TREND_LINEAR, $yValues, $xValues);
108
109	5	return $bestFitLinear->getCovariance();
110		}
111
112		/**
113		* FORECAST.
114		*
115		* Calculates, or predicts, a future value by using existing values.
116		* The predicted value is a y-value for a given x-value.
117		*
118		* @param mixed $xValue Float value of X for which we want to find Y
119		* Or can be an array of values
120		* @param mixed $yValues array of mixed Data Series Y
121		* @param mixed $xValues of mixed Data Series X
122		*
123		* @return array\|bool\|float\|string
124		* If an array of numbers is passed as an argument, then the returned result will also be an array
125		* with the same dimensions
126		*/
127	15	public static function FORECAST($xValue, $yValues, $xValues)
128		{
129	15	if (is_array($xValue)) {
130	3	return self::evaluateArrayArgumentsSubset([self::class, __FUNCTION__], 1, $xValue, $yValues, $xValues);
131		}
132
133		try {
134	15	$xValue = StatisticalValidations::validateFloat($xValue);
135	14	self::checkTrendArrays($yValues, $xValues);
136	14	self::validateTrendArrays($yValues, $xValues);
137	3	} catch (Exception $e) {
138	3	return $e->getMessage();
139		}
140
141	12	$bestFitLinear = Trend::calculate(Trend::TREND_LINEAR, $yValues, $xValues);
142
143	12	return $bestFitLinear->getValueOfYForX($xValue);
144		}
145
146		/**
147		* GROWTH.
148		*
149		* Returns values along a predicted exponential Trend
150		*
151		* @param mixed[] $yValues Data Series Y
152		* @param mixed[] $xValues Data Series X
153		* @param mixed[] $newValues Values of X for which we want to find Y
154		* @param mixed $const A logical (boolean) value specifying whether to force the intersect to equal 0 or not
155		*
156		* @return float[]
157		*/
158	3	public static function GROWTH($yValues, $xValues = [], $newValues = [], $const = true)
159		{
160	3	$yValues = Functions::flattenArray($yValues);
161	3	$xValues = Functions::flattenArray($xValues);
162	3	$newValues = Functions::flattenArray($newValues);
163	3	$const = ($const === null) ? true : (bool) Functions::flattenSingleValue($const);
164
165	3	$bestFitExponential = Trend::calculate(Trend::TREND_EXPONENTIAL, $yValues, $xValues, $const);
166	3	if (empty($newValues)) {
167	2	$newValues = $bestFitExponential->getXValues();
168		}
169
170	3	$returnArray = [];
171	3	foreach ($newValues as $xValue) {
172	3	$returnArray[0][] = [$bestFitExponential->getValueOfYForX($xValue)];
173		}
174
175	3	return $returnArray; //* @phpstan-ignore-line
176		}
177
178		/**
179		* INTERCEPT.
180		*
181		* Calculates the point at which a line will intersect the y-axis by using existing x-values and y-values.
182		*
183		* @param mixed[] $yValues Data Series Y
184		* @param mixed[] $xValues Data Series X
185		*
186		* @return float\|string
187		*/
188	9	public static function INTERCEPT($yValues, $xValues)
189		{
190		try {
191	9	self::checkTrendArrays($yValues, $xValues);
192	9	self::validateTrendArrays($yValues, $xValues);
193	2	} catch (Exception $e) {
194	2	return $e->getMessage();
195		}
196
197	7	$bestFitLinear = Trend::calculate(Trend::TREND_LINEAR, $yValues, $xValues);
198
199	7	return $bestFitLinear->getIntersect();
200		}
201
202		/**
203		* LINEST.
204		*
205		* Calculates the statistics for a line by using the "least squares" method to calculate a straight line
206		* that best fits your data, and then returns an array that describes the line.
207		*
208		* @param mixed[] $yValues Data Series Y
209		* @param null\|mixed[] $xValues Data Series X
210		* @param mixed $const A logical (boolean) value specifying whether to force the intersect to equal 0 or not
211		* @param mixed $stats A logical (boolean) value specifying whether to return additional regression statistics
212		*
213		* @return array\|int\|string The result, or a string containing an error
214		*/
215	9	public static function LINEST($yValues, $xValues = null, $const = true, $stats = false)
216		{
217	9	$const = ($const === null) ? true : (bool) Functions::flattenSingleValue($const);
218	9	$stats = ($stats === null) ? false : (bool) Functions::flattenSingleValue($stats);
219	9	if ($xValues === null) {
220		$xValues = $yValues;
221		}
222
223		try {
224	9	self::checkTrendArrays($yValues, $xValues);
225	9	self::validateTrendArrays($yValues, $xValues);
226		} catch (Exception $e) {
227		return $e->getMessage();
228		}
229
230	9	$bestFitLinear = Trend::calculate(Trend::TREND_LINEAR, $yValues, $xValues, $const);
231
232	9	if ($stats === true) {
233	3	return [
234	3	[
235	3	$bestFitLinear->getSlope(),
236	3	$bestFitLinear->getIntersect(),
237	3	],
238	3	[
239	3	$bestFitLinear->getSlopeSE(),
240	3	($const === false) ? ExcelError::NA() : $bestFitLinear->getIntersectSE(),
241	3	],
242	3	[
243	3	$bestFitLinear->getGoodnessOfFit(),
244	3	$bestFitLinear->getStdevOfResiduals(),
245	3	],
246	3	[
247	3	$bestFitLinear->getF(),
248	3	$bestFitLinear->getDFResiduals(),
249	3	],
250	3	[
251	3	$bestFitLinear->getSSRegression(),
252	3	$bestFitLinear->getSSResiduals(),
253	3	],
254	3	];
255		}
256
257	6	return [
258	6	$bestFitLinear->getSlope(),
259	6	$bestFitLinear->getIntersect(),
260	6	];
261		}
262
263		/**
264		* LOGEST.
265		*
266		* Calculates an exponential curve that best fits the X and Y data series,
267		* and then returns an array that describes the line.
268		*
269		* @param mixed[] $yValues Data Series Y
270		* @param null\|mixed[] $xValues Data Series X
271		* @param mixed $const A logical (boolean) value specifying whether to force the intersect to equal 0 or not
272		* @param mixed $stats A logical (boolean) value specifying whether to return additional regression statistics
273		*
274		* @return array\|int\|string The result, or a string containing an error
275		*/
276	7	public static function LOGEST($yValues, $xValues = null, $const = true, $stats = false)
277		{
278	7	$const = ($const === null) ? true : (bool) Functions::flattenSingleValue($const);
279	7	$stats = ($stats === null) ? false : (bool) Functions::flattenSingleValue($stats);
280	7	if ($xValues === null) {
281		$xValues = $yValues;
282		}
283
284		try {
285	7	self::checkTrendArrays($yValues, $xValues);
286	7	self::validateTrendArrays($yValues, $xValues);
287		} catch (Exception $e) {
288		return $e->getMessage();
289		}
290
291	7	foreach ($yValues as $value) {
292	7	if ($value < 0.0) {
293		return ExcelError::NAN();
294		}
295		}
296
297	7	$bestFitExponential = Trend::calculate(Trend::TREND_EXPONENTIAL, $yValues, $xValues, $const);
298
299	7	if ($stats === true) {
300		return [
301		[
302		$bestFitExponential->getSlope(),
303		$bestFitExponential->getIntersect(),
304		],
305		[
306		$bestFitExponential->getSlopeSE(),
307		($const === false) ? ExcelError::NA() : $bestFitExponential->getIntersectSE(),
308		],
309		[
310		$bestFitExponential->getGoodnessOfFit(),
311		$bestFitExponential->getStdevOfResiduals(),
312		],
313		[
314		$bestFitExponential->getF(),
315		$bestFitExponential->getDFResiduals(),
316		],
317		[
318		$bestFitExponential->getSSRegression(),
319		$bestFitExponential->getSSResiduals(),
320		],
321		];
322		}
323
324	7	return [
325	7	$bestFitExponential->getSlope(),
326	7	$bestFitExponential->getIntersect(),
327	7	];
328		}
329
330		/**
331		* RSQ.
332		*
333		* Returns the square of the Pearson product moment correlation coefficient through data points
334		* in known_y's and known_x's.
335		*
336		* @param mixed[] $yValues Data Series Y
337		* @param mixed[] $xValues Data Series X
338		*
339		* @return float\|string The result, or a string containing an error
340		*/
341	6	public static function RSQ($yValues, $xValues)
342		{
343		try {
344	6	self::checkTrendArrays($yValues, $xValues);
345	6	self::validateTrendArrays($yValues, $xValues);
346	2	} catch (Exception $e) {
347	2	return $e->getMessage();
348		}
349
350	4	$bestFitLinear = Trend::calculate(Trend::TREND_LINEAR, $yValues, $xValues);
351
352	4	return $bestFitLinear->getGoodnessOfFit();
353		}
354
355		/**
356		* SLOPE.
357		*
358		* Returns the slope of the linear regression line through data points in known_y's and known_x's.
359		*
360		* @param mixed[] $yValues Data Series Y
361		* @param mixed[] $xValues Data Series X
362		*
363		* @return float\|string The result, or a string containing an error
364		*/
365	10	public static function SLOPE($yValues, $xValues)
366		{
367		try {
368	10	self::checkTrendArrays($yValues, $xValues);
369	10	self::validateTrendArrays($yValues, $xValues);
370	2	} catch (Exception $e) {
371	2	return $e->getMessage();
372		}
373
374	8	$bestFitLinear = Trend::calculate(Trend::TREND_LINEAR, $yValues, $xValues);
375
376	8	return $bestFitLinear->getSlope();
377		}
378
379		/**
380		* STEYX.
381		*
382		* Returns the standard error of the predicted y-value for each x in the regression.
383		*
384		* @param mixed[] $yValues Data Series Y
385		* @param mixed[] $xValues Data Series X
386		*
387		* @return float\|string
388		*/
389	6	public static function STEYX($yValues, $xValues)
390		{
391		try {
392	6	self::checkTrendArrays($yValues, $xValues);
393	6	self::validateTrendArrays($yValues, $xValues);
394	2	} catch (Exception $e) {
395	2	return $e->getMessage();
396		}
397
398	4	$bestFitLinear = Trend::calculate(Trend::TREND_LINEAR, $yValues, $xValues);
399
400	4	return $bestFitLinear->getStdevOfResiduals();
401		}
402
403		/**
404		* TREND.
405		*
406		* Returns values along a linear Trend
407		*
408		* @param mixed[] $yValues Data Series Y
409		* @param mixed[] $xValues Data Series X
410		* @param mixed[] $newValues Values of X for which we want to find Y
411		* @param mixed $const A logical (boolean) value specifying whether to force the intersect to equal 0 or not
412		*
413		* @return float[]
414		*/
415	3	public static function TREND($yValues, $xValues = [], $newValues = [], $const = true)
416		{
417	3	$yValues = Functions::flattenArray($yValues);
418	3	$xValues = Functions::flattenArray($xValues);
419	3	$newValues = Functions::flattenArray($newValues);
420	3	$const = ($const === null) ? true : (bool) Functions::flattenSingleValue($const);
421
422	3	$bestFitLinear = Trend::calculate(Trend::TREND_LINEAR, $yValues, $xValues, $const);
423	3	if (empty($newValues)) {
424	2	$newValues = $bestFitLinear->getXValues();
425		}
426
427	3	$returnArray = [];
428	3	foreach ($newValues as $xValue) {
429	3	$returnArray[0][] = [$bestFitLinear->getValueOfYForX($xValue)];
430		}
431
432	3	return $returnArray; //* @phpstan-ignore-line
433		}
434		}
435

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