<?php
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namespace PhpOffice\PhpSpreadsheet\Calculation\Statistical;
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use PhpOffice\PhpSpreadsheet\Calculation\ArrayEnabled;
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use PhpOffice\PhpSpreadsheet\Calculation\Exception;
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use PhpOffice\PhpSpreadsheet\Calculation\Functions;
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use PhpOffice\PhpSpreadsheet\Calculation\Information\ExcelError;
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use PhpOffice\PhpSpreadsheet\Shared\Trend\Trend;
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class Trends
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{
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use ArrayEnabled;
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private static function filterTrendValues(array &$array1, array &$array2): void
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{
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foreach ($array1 as $key => $value) {
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if ((is_bool($value)) || (is_string($value)) || ($value === null)) {
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unset($array1[$key], $array2[$key]);
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}
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}
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}
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/**
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* @param mixed $array1 should be array, but scalar is made into one
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* @param mixed $array2 should be array, but scalar is made into one
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*/
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private static function checkTrendArrays(mixed &$array1, mixed &$array2): void
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{
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if (!is_array($array1)) {
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$array1 = [$array1];
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}
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if (!is_array($array2)) {
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$array2 = [$array2];
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}
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$array1 = Functions::flattenArray($array1);
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$array2 = Functions::flattenArray($array2);
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self::filterTrendValues($array1, $array2);
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self::filterTrendValues($array2, $array1);
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// Reset the array indexes
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$array1 = array_merge($array1);
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$array2 = array_merge($array2);
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}
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protected static function validateTrendArrays(array $yValues, array $xValues): void
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{
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$yValueCount = count($yValues);
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$xValueCount = count($xValues);
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if (($yValueCount === 0) || ($yValueCount !== $xValueCount)) {
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throw new Exception(ExcelError::NA());
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} elseif ($yValueCount === 1) {
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throw new Exception(ExcelError::DIV0());
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}
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}
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/**
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* CORREL.
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*
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* Returns covariance, the average of the products of deviations for each data point pair.
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*
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* @param mixed $yValues array of mixed Data Series Y
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* @param null|mixed $xValues array of mixed Data Series X
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*/
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public static function CORREL(mixed $yValues, $xValues = null): float|string
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{
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if (($xValues === null) || (!is_array($yValues)) || (!is_array($xValues))) {
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return ExcelError::VALUE();
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}
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try {
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self::checkTrendArrays($yValues, $xValues);
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self::validateTrendArrays($yValues, $xValues);
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} catch (Exception $e) {
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return $e->getMessage();
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}
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$bestFitLinear = Trend::calculate(Trend::TREND_LINEAR, $yValues, $xValues);
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return $bestFitLinear->getCorrelation();
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}
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/**
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* COVAR.
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*
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* Returns covariance, the average of the products of deviations for each data point pair.
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*
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* @param mixed[] $yValues array of mixed Data Series Y
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* @param mixed[] $xValues array of mixed Data Series X
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*/
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public static function COVAR(array $yValues, array $xValues): float|string
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{
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try {
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self::checkTrendArrays($yValues, $xValues);
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self::validateTrendArrays($yValues, $xValues);
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} catch (Exception $e) {
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return $e->getMessage();
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}
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$bestFitLinear = Trend::calculate(Trend::TREND_LINEAR, $yValues, $xValues);
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return $bestFitLinear->getCovariance();
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}
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/**
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* FORECAST.
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*
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* Calculates, or predicts, a future value by using existing values.
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* The predicted value is a y-value for a given x-value.
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*
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* @param mixed $xValue Float value of X for which we want to find Y
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* Or can be an array of values
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* @param mixed[] $yValues array of mixed Data Series Y
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* @param mixed[] $xValues array of mixed Data Series X
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*
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* @return array|bool|float|string If an array of numbers is passed as an argument, then the returned result will also be an array
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* with the same dimensions
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*/
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public static function FORECAST(mixed $xValue, array $yValues, array $xValues)
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{
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if (is_array($xValue)) {
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return self::evaluateArrayArgumentsSubset([self::class, __FUNCTION__], 1, $xValue, $yValues, $xValues);
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}
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try {
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$xValue = StatisticalValidations::validateFloat($xValue);
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self::checkTrendArrays($yValues, $xValues);
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self::validateTrendArrays($yValues, $xValues);
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} catch (Exception $e) {
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return $e->getMessage();
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}
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$bestFitLinear = Trend::calculate(Trend::TREND_LINEAR, $yValues, $xValues);
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return $bestFitLinear->getValueOfYForX($xValue);
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}
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/**
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* GROWTH.
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*
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* Returns values along a predicted exponential Trend
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*
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* @param mixed[] $yValues Data Series Y
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* @param mixed[] $xValues Data Series X
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* @param mixed[] $newValues Values of X for which we want to find Y
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* @param mixed $const A logical (boolean) value specifying whether to force the intersect to equal 0 or not
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*
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* @return array<int, array<int, array<int, float>>>
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*/
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public static function GROWTH(array $yValues, array $xValues = [], array $newValues = [], mixed $const = true): array
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{
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$yValues = Functions::flattenArray($yValues);
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$xValues = Functions::flattenArray($xValues);
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$newValues = Functions::flattenArray($newValues);
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$const = ($const === null) ? true : (bool) Functions::flattenSingleValue($const);
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$bestFitExponential = Trend::calculate(Trend::TREND_EXPONENTIAL, $yValues, $xValues, $const);
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if (empty($newValues)) {
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$newValues = $bestFitExponential->getXValues();
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}
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$returnArray = [];
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foreach ($newValues as $xValue) {
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$returnArray[0][] = [$bestFitExponential->getValueOfYForX($xValue)];
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}
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return $returnArray;
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}
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/**
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* INTERCEPT.
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*
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* Calculates the point at which a line will intersect the y-axis by using existing x-values and y-values.
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*
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* @param mixed[] $yValues Data Series Y
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* @param mixed[] $xValues Data Series X
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*/
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public static function INTERCEPT(array $yValues, array $xValues): float|string
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{
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try {
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self::checkTrendArrays($yValues, $xValues);
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self::validateTrendArrays($yValues, $xValues);
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} catch (Exception $e) {
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return $e->getMessage();
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}
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$bestFitLinear = Trend::calculate(Trend::TREND_LINEAR, $yValues, $xValues);
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return $bestFitLinear->getIntersect();
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}
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/**
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* LINEST.
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*
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* Calculates the statistics for a line by using the "least squares" method to calculate a straight line
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* that best fits your data, and then returns an array that describes the line.
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*
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* @param mixed[] $yValues Data Series Y
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* @param null|mixed[] $xValues Data Series X
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* @param mixed $const A logical (boolean) value specifying whether to force the intersect to equal 0 or not
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* @param mixed $stats A logical (boolean) value specifying whether to return additional regression statistics
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*
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* @return array|string The result, or a string containing an error
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*/
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public static function LINEST(array $yValues, ?array $xValues = null, mixed $const = true, mixed $stats = false): string|array
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{
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$const = ($const === null) ? true : (bool) Functions::flattenSingleValue($const);
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$stats = ($stats === null) ? false : (bool) Functions::flattenSingleValue($stats);
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if ($xValues === null) {
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$xValues = $yValues;
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}
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try {
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self::checkTrendArrays($yValues, $xValues);
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self::validateTrendArrays($yValues, $xValues);
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} catch (Exception $e) {
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return $e->getMessage();
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}
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$bestFitLinear = Trend::calculate(Trend::TREND_LINEAR, $yValues, $xValues, $const);
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if ($stats === true) {
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return [
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[
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$bestFitLinear->getSlope(),
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$bestFitLinear->getIntersect(),
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],
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[
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$bestFitLinear->getSlopeSE(),
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($const === false) ? ExcelError::NA() : $bestFitLinear->getIntersectSE(),
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],
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[
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$bestFitLinear->getGoodnessOfFit(),
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$bestFitLinear->getStdevOfResiduals(),
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],
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[
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$bestFitLinear->getF(),
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$bestFitLinear->getDFResiduals(),
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],
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[
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$bestFitLinear->getSSRegression(),
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$bestFitLinear->getSSResiduals(),
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],
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];
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}
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return [
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$bestFitLinear->getSlope(),
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$bestFitLinear->getIntersect(),
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];
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}
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/**
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* LOGEST.
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*
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* Calculates an exponential curve that best fits the X and Y data series,
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* and then returns an array that describes the line.
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*
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* @param mixed[] $yValues Data Series Y
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* @param null|mixed[] $xValues Data Series X
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* @param mixed $const A logical (boolean) value specifying whether to force the intersect to equal 0 or not
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* @param mixed $stats A logical (boolean) value specifying whether to return additional regression statistics
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*
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* @return array|string The result, or a string containing an error
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*/
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public static function LOGEST(array $yValues, ?array $xValues = null, mixed $const = true, mixed $stats = false): string|array
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{
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$const = ($const === null) ? true : (bool) Functions::flattenSingleValue($const);
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$stats = ($stats === null) ? false : (bool) Functions::flattenSingleValue($stats);
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if ($xValues === null) {
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$xValues = $yValues;
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}
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try {
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self::checkTrendArrays($yValues, $xValues);
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self::validateTrendArrays($yValues, $xValues);
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} catch (Exception $e) {
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return $e->getMessage();
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}
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foreach ($yValues as $value) {
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if ($value < 0.0) {
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return ExcelError::NAN();
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}
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}
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$bestFitExponential = Trend::calculate(Trend::TREND_EXPONENTIAL, $yValues, $xValues, $const);
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if ($stats === true) {
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return [
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[
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$bestFitExponential->getSlope(),
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$bestFitExponential->getIntersect(),
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],
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[
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$bestFitExponential->getSlopeSE(),
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($const === false) ? ExcelError::NA() : $bestFitExponential->getIntersectSE(),
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],
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[
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$bestFitExponential->getGoodnessOfFit(),
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$bestFitExponential->getStdevOfResiduals(),
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],
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[
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$bestFitExponential->getF(),
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$bestFitExponential->getDFResiduals(),
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],
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[
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$bestFitExponential->getSSRegression(),
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$bestFitExponential->getSSResiduals(),
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],
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];
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}
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return [
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$bestFitExponential->getSlope(),
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$bestFitExponential->getIntersect(),
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];
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}
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/**
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* RSQ.
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*
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* Returns the square of the Pearson product moment correlation coefficient through data points
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* in known_y's and known_x's.
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*
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* @param mixed[] $yValues Data Series Y
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* @param mixed[] $xValues Data Series X
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*
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* @return float|string The result, or a string containing an error
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*/
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public static function RSQ(array $yValues, array $xValues)
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{
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try {
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self::checkTrendArrays($yValues, $xValues);
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self::validateTrendArrays($yValues, $xValues);
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} catch (Exception $e) {
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return $e->getMessage();
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}
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$bestFitLinear = Trend::calculate(Trend::TREND_LINEAR, $yValues, $xValues);
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return $bestFitLinear->getGoodnessOfFit();
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}
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/**
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* SLOPE.
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*
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* Returns the slope of the linear regression line through data points in known_y's and known_x's.
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*
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* @param mixed[] $yValues Data Series Y
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* @param mixed[] $xValues Data Series X
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*
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* @return float|string The result, or a string containing an error
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*/
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public static function SLOPE(array $yValues, array $xValues)
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{
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try {
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self::checkTrendArrays($yValues, $xValues);
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self::validateTrendArrays($yValues, $xValues);
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} catch (Exception $e) {
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return $e->getMessage();
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}
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$bestFitLinear = Trend::calculate(Trend::TREND_LINEAR, $yValues, $xValues);
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return $bestFitLinear->getSlope();
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}
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/**
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* STEYX.
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*
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* Returns the standard error of the predicted y-value for each x in the regression.
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*
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* @param mixed[] $yValues Data Series Y
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* @param mixed[] $xValues Data Series X
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*/
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public static function STEYX(array $yValues, array $xValues): float|string
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{
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try {
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self::checkTrendArrays($yValues, $xValues);
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self::validateTrendArrays($yValues, $xValues);
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} catch (Exception $e) {
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return $e->getMessage();
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}
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$bestFitLinear = Trend::calculate(Trend::TREND_LINEAR, $yValues, $xValues);
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return $bestFitLinear->getStdevOfResiduals();
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}
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/**
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* TREND.
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*
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* Returns values along a linear Trend
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*
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* @param mixed[] $yValues Data Series Y
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* @param mixed[] $xValues Data Series X
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* @param mixed[] $newValues Values of X for which we want to find Y
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* @param mixed $const A logical (boolean) value specifying whether to force the intersect to equal 0 or not
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*
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* @return array<int, array<int, array<int, float>>>
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*/
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public static function TREND(array $yValues, array $xValues = [], array $newValues = [], mixed $const = true): array
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{
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$yValues = Functions::flattenArray($yValues);
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$xValues = Functions::flattenArray($xValues);
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$newValues = Functions::flattenArray($newValues);
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$const = ($const === null) ? true : (bool) Functions::flattenSingleValue($const);
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$bestFitLinear = Trend::calculate(Trend::TREND_LINEAR, $yValues, $xValues, $const);
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if (empty($newValues)) {
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$newValues = $bestFitLinear->getXValues();
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}
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$returnArray = [];
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foreach ($newValues as $xValue) {
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$returnArray[0][] = [$bestFitLinear->getValueOfYForX($xValue)];
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}
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return $returnArray;
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}
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}
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