BestFit::calculateGoodnessOfFit() - Code Metrics - Inspection of "PHPStan Level 3" - PHPOffice/PhpSpreadsheet - Measure and Improve Code Quality continuously with Scrutinizer

Failed Conditions

Push — master ( 02f37d...f95322 )

by Adrien

created 2021-04-11 07:52 UTC

BestFit::calculateGoodnessOfFit() C

↳ Parent: BestFit

Complexity

Conditions	11
Paths	160

Size

Total Lines	50
Code Lines	38

Duplication

Lines	0
Ratio	0 %

Code Coverage

Tests	30
CRAP Score	11.0295

Importance

Changes

Metric	Value
cc	11
eloc	38
c	0
b	0
f	0
nc	160
nop	8
dl	0
loc	50
ccs	30
cts	32
cp	0.9375
crap	11.0295
rs	6.8166

How to fix Complexity Many Parameters

<?php

namespace PhpOffice\PhpSpreadsheet\Shared\Trend;

abstract class BestFit
{
    /**
     * Indicator flag for a calculation error.
     *
     * @var bool
     */
    protected $error = false;

    /**
     * Algorithm type to use for best-fit.
     *
     * @var string
     */
    protected $bestFitType = 'undetermined';

    /**
     * Number of entries in the sets of x- and y-value arrays.
     *
     * @var int
     */
    protected $valueCount = 0;

    /**
     * X-value dataseries of values.
     *
     * @var float[]
     */
    protected $xValues = [];

    /**
     * Y-value dataseries of values.
     *
     * @var float[]
     */
    protected $yValues = [];

    /**
     * Flag indicating whether values should be adjusted to Y=0.
     *
     * @var bool
     */
    protected $adjustToZero = false;

    /**
     * Y-value series of best-fit values.
     *
     * @var float[]
     */
    protected $yBestFitValues = [];

    protected $goodnessOfFit = 1;

    protected $stdevOfResiduals = 0;

    protected $covariance = 0;

    protected $correlation = 0;

    protected $SSRegression = 0;

    protected $SSResiduals = 0;

    protected $DFResiduals = 0;

    protected $f = 0;

    protected $slope = 0;

    protected $slopeSE = 0;

    protected $intersect = 0;

    protected $intersectSE = 0;

    protected $xOffset = 0;

    protected $yOffset = 0;

    public function getError()
    {
        return $this->error;
    }

    public function getBestFitType()
    {
        return $this->bestFitType;
    }

    /**
     * Return the Y-Value for a specified value of X.
     *
     * @param float $xValue X-Value
     *
     * @return float Y-Value
     */
    abstract public function getValueOfYForX($xValue);

    /**
     * Return the X-Value for a specified value of Y.
     *
     * @param float $yValue Y-Value
     *
     * @return float X-Value
     */
    abstract public function getValueOfXForY($yValue);

    /**
     * Return the original set of X-Values.
     *
     * @return float[] X-Values
     */
    public function getXValues()
    {
        return $this->xValues;
    }

    /**
     * Return the Equation of the best-fit line.
     *
     * @param int $dp Number of places of decimal precision to display
     *
     * @return string
     */
    abstract public function getEquation($dp = 0);

    /**
     * Return the Slope of the line.
     *
     * @param int $dp Number of places of decimal precision to display
     *
     * @return float
     */
    public function getSlope($dp = 0)
    {
        if ($dp != 0) {
            return round($this->slope, $dp);
        }

        return $this->slope;
    }

    /**
     * Return the standard error of the Slope.
     *
     * @param int $dp Number of places of decimal precision to display
     *
     * @return float
     */
    public function getSlopeSE($dp = 0)
    {
        if ($dp != 0) {
            return round($this->slopeSE, $dp);
        }

        return $this->slopeSE;
    }

    /**
     * Return the Value of X where it intersects Y = 0.
     *
     * @param int $dp Number of places of decimal precision to display
     *
     * @return float
     */
    public function getIntersect($dp = 0)
    {
        if ($dp != 0) {
            return round($this->intersect, $dp);
        }

        return $this->intersect;
    }

    /**
     * Return the standard error of the Intersect.
     *
     * @param int $dp Number of places of decimal precision to display
     *
     * @return float
     */
    public function getIntersectSE($dp = 0)
    {
        if ($dp != 0) {
            return round($this->intersectSE, $dp);
        }

        return $this->intersectSE;
    }

    /**
     * Return the goodness of fit for this regression.
     *
     * @param int $dp Number of places of decimal precision to return
     *
     * @return float
     */
    public function getGoodnessOfFit($dp = 0)
    {
        if ($dp != 0) {
            return round($this->goodnessOfFit, $dp);
        }

        return $this->goodnessOfFit;
    }

    /**
     * Return the goodness of fit for this regression.
     *
     * @param int $dp Number of places of decimal precision to return
     *
     * @return float
     */
    public function getGoodnessOfFitPercent($dp = 0)
    {
        if ($dp != 0) {
            return round($this->goodnessOfFit * 100, $dp);
        }

        return $this->goodnessOfFit * 100;
    }

    /**
     * Return the standard deviation of the residuals for this regression.
     *
     * @param int $dp Number of places of decimal precision to return
     *
     * @return float
     */
    public function getStdevOfResiduals($dp = 0)
    {
        if ($dp != 0) {
            return round($this->stdevOfResiduals, $dp);
        }

        return $this->stdevOfResiduals;
    }

    /**
     * @param int $dp Number of places of decimal precision to return
     *
     * @return float
     */
    public function getSSRegression($dp = 0)
    {
        if ($dp != 0) {
            return round($this->SSRegression, $dp);
        }

        return $this->SSRegression;
    }

    /**
     * @param int $dp Number of places of decimal precision to return
     *
     * @return float
     */
    public function getSSResiduals($dp = 0)
    {
        if ($dp != 0) {
            return round($this->SSResiduals, $dp);
        }

        return $this->SSResiduals;
    }

    /**
     * @param int $dp Number of places of decimal precision to return
     *
     * @return float
     */
    public function getDFResiduals($dp = 0)
    {
        if ($dp != 0) {
            return round($this->DFResiduals, $dp);
        }

        return $this->DFResiduals;
    }

    /**
     * @param int $dp Number of places of decimal precision to return
     *
     * @return float
     */
    public function getF($dp = 0)
    {
        if ($dp != 0) {
            return round($this->f, $dp);
        }

        return $this->f;
    }

    /**
     * @param int $dp Number of places of decimal precision to return
     *
     * @return float
     */
    public function getCovariance($dp = 0)
    {
        if ($dp != 0) {
            return round($this->covariance, $dp);
        }

        return $this->covariance;
    }

    /**
     * @param int $dp Number of places of decimal precision to return
     *
     * @return float
     */
    public function getCorrelation($dp = 0)
    {
        if ($dp != 0) {
            return round($this->correlation, $dp);
        }

        return $this->correlation;
    }

    /**
     * @return float[]
     */
    public function getYBestFitValues()
    {
        return $this->yBestFitValues;
    }

    protected function calculateGoodnessOfFit($sumX, $sumY, $sumX2, $sumY2, $sumXY, $meanX, $meanY, $const): void
    {
        $SSres = $SScov = $SScor = $SStot = $SSsex = 0.0;

        foreach ($this->xValues as $xKey => $xValue) {
            $bestFitY = $this->yBestFitValues[$xKey] = $this->getValueOfYForX($xValue);

            $SSres += ($this->yValues[$xKey] - $bestFitY) * ($this->yValues[$xKey] - $bestFitY);
            if ($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 === true) {
                $SSsex += ($this->xValues[$xKey] - $meanX) * ($this->xValues[$xKey] - $meanX);
            } else {
                $SSsex += $this->xValues[$xKey] * $this->xValues[$xKey];
            }
        }

        $this->SSResiduals = $SSres;
        $this->DFResiduals = $this->valueCount - 1 - ($const === true ? 1 : 0);

        if ($this->DFResiduals == 0.0) {
            $this->stdevOfResiduals = 0.0;
        } else {
            $this->stdevOfResiduals = sqrt($SSres / $this->DFResiduals);
        }
        if (($SStot == 0.0) || ($SSres == $SStot)) {

            $this->goodnessOfFit = 1;
        } else {
            $this->goodnessOfFit = 1 - ($SSres / $SStot);
        }

        $this->SSRegression = $this->goodnessOfFit * $SStot;
        $this->covariance = $SScov / $this->valueCount;
        $this->correlation = ($this->valueCount * $sumXY - $sumX * $sumY) / sqrt(($this->valueCount * $sumX2 - $sumX ** 2) * ($this->valueCount * $sumY2 - $sumY ** 2));
        $this->slopeSE = $this->stdevOfResiduals / sqrt($SSsex);
        $this->intersectSE = $this->stdevOfResiduals * sqrt(1 / ($this->valueCount - ($sumX * $sumX) / $sumX2));
        if ($this->SSResiduals != 0.0) {
            if ($this->DFResiduals == 0.0) {
                $this->f = 0.0;
            } else {
                $this->f = $this->SSRegression / ($this->SSResiduals / $this->DFResiduals);
            }
        } else {
            if ($this->DFResiduals == 0.0) {
                $this->f = 0.0;
            } else {
                $this->f = $this->SSRegression / $this->DFResiduals;
            }
        }
    }

    private function sumSquares(array $values)
    {
        return array_sum(
            array_map(
                function ($value) {
                    return $value ** 2;
                },
                $values
            )
        );
    }

    /**
     * @param float[] $yValues
     * @param float[] $xValues
     */
    protected function leastSquareFit(array $yValues, array $xValues, bool $const): void
    {
        // calculate sums
        $sumValuesX = array_sum($xValues);
        $sumValuesY = array_sum($yValues);
        $meanValueX = $sumValuesX / $this->valueCount;
        $meanValueY = $sumValuesY / $this->valueCount;
        $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];

            if ($const === true) {
                $mBase += ($xValues[$i] - $meanValueX) * ($yValues[$i] - $meanValueY);
                $mDivisor += ($xValues[$i] - $meanValueX) * ($xValues[$i] - $meanValueX);
            } else {
                $mBase += $xValues[$i] * $yValues[$i];
                $mDivisor += $xValues[$i] * $xValues[$i];
            }
        }

        // calculate slope
        $this->slope = $mBase / $mDivisor;

        // calculate intersect
        $this->intersect = ($const === true) ? $meanValueY - ($this->slope * $meanValueX) : 0.0;

        $this->calculateGoodnessOfFit($sumValuesX, $sumValuesY, $sumSquaresX, $sumSquaresY, $xy_sum, $meanValueX, $meanValueY, $const);
    }

    /**
     * Define the regression.
     *
     * @param float[] $yValues The set of Y-values for this regression
     * @param float[] $xValues The set of X-values for this regression
     */
    public function __construct($yValues, $xValues = [])
    {
        //    Calculate number of points
        $yValueCount = count($yValues);
        $xValueCount = count($xValues);

        //    Define X Values if necessary
        if ($xValueCount === 0) {
            $xValues = range(1, $yValueCount);
        } elseif ($yValueCount !== $xValueCount) {
            //    Ensure both arrays of points are the same size
            $this->error = true;
        }

        $this->valueCount = $yValueCount;
        $this->xValues = $xValues;
        $this->yValues = $yValues;
    }
}


1		<?php
2
3		namespace PhpOffice\PhpSpreadsheet\Shared\Trend;
4
5		abstract class BestFit
6		{
7		/**
8		* Indicator flag for a calculation error.
9		*
10		* @var bool
11		*/
12		protected $error = false;
13
14		/**
15		* Algorithm type to use for best-fit.
16		*
17		* @var string
18		*/
19		protected $bestFitType = 'undetermined';
20
21		/**
22		* Number of entries in the sets of x- and y-value arrays.
23		*
24		* @var int
25		*/
26		protected $valueCount = 0;
27
28		/**
29		* X-value dataseries of values.
30		*
31		* @var float[]
32		*/
33		protected $xValues = [];
34
35		/**
36		* Y-value dataseries of values.
37		*
38		* @var float[]
39		*/
40		protected $yValues = [];
41
42		/**
43		* Flag indicating whether values should be adjusted to Y=0.
44		*
45		* @var bool
46		*/
47		protected $adjustToZero = false;
48
49		/**
50		* Y-value series of best-fit values.
51		*
52		* @var float[]
53		*/
54		protected $yBestFitValues = [];
55
56		protected $goodnessOfFit = 1;
57
58		protected $stdevOfResiduals = 0;
59
60		protected $covariance = 0;
61
62		protected $correlation = 0;
63
64		protected $SSRegression = 0;
65
66		protected $SSResiduals = 0;
67
68		protected $DFResiduals = 0;
69
70		protected $f = 0;
71
72		protected $slope = 0;
73
74		protected $slopeSE = 0;
75
76		protected $intersect = 0;
77
78		protected $intersectSE = 0;
79
80		protected $xOffset = 0;
81
82		protected $yOffset = 0;
83
84		public function getError()
85		{
86		return $this->error;
87		}
88
89		public function getBestFitType()
90		{
91		return $this->bestFitType;
92		}
93
94		/**
95		* Return the Y-Value for a specified value of X.
96		*
97		* @param float $xValue X-Value
98		*
99		* @return float Y-Value
100		*/
101		abstract public function getValueOfYForX($xValue);
102
103		/**
104		* Return the X-Value for a specified value of Y.
105		*
106		* @param float $yValue Y-Value
107		*
108		* @return float X-Value
109		*/
110		abstract public function getValueOfXForY($yValue);
111
112		/**
113		* Return the original set of X-Values.
114		*
115		* @return float[] X-Values
116		*/
117	2	public function getXValues()
118		{
119	2	return $this->xValues;
120		}
121
122		/**
123		* Return the Equation of the best-fit line.
124		*
125		* @param int $dp Number of places of decimal precision to display
126		*
127		* @return string
128		*/
129		abstract public function getEquation($dp = 0);
130
131		/**
132		* Return the Slope of the line.
133		*
134		* @param int $dp Number of places of decimal precision to display
135		*
136		* @return float
137		*/
138	37	public function getSlope($dp = 0)
139		{
140	37	if ($dp != 0) {
141	2	return round($this->slope, $dp);
142		}
143
144	37	return $this->slope;
145		}
146
147		/**
148		* Return the standard error of the Slope.
149		*
150		* @param int $dp Number of places of decimal precision to display
151		*
152		* @return float
153		*/
154	3	public function getSlopeSE($dp = 0)
155		{
156	3	if ($dp != 0) {
157		return round($this->slopeSE, $dp);
158		}
159
160	3	return $this->slopeSE;
161		}
162
163		/**
164		* Return the Value of X where it intersects Y = 0.
165		*
166		* @param int $dp Number of places of decimal precision to display
167		*
168		* @return float
169		*/
170	36	public function getIntersect($dp = 0)
171		{
172	36	if ($dp != 0) {
173	2	return round($this->intersect, $dp);
174		}
175
176	36	return $this->intersect;
177		}
178
179		/**
180		* Return the standard error of the Intersect.
181		*
182		* @param int $dp Number of places of decimal precision to display
183		*
184		* @return float
185		*/
186	2	public function getIntersectSE($dp = 0)
187		{
188	2	if ($dp != 0) {
189		return round($this->intersectSE, $dp);
190		}
191
192	2	return $this->intersectSE;
193		}
194
195		/**
196		* Return the goodness of fit for this regression.
197		*
198		* @param int $dp Number of places of decimal precision to return
199		*
200		* @return float
201		*/
202	8	public function getGoodnessOfFit($dp = 0)
203		{
204	8	if ($dp != 0) {
205	3	return round($this->goodnessOfFit, $dp);
206		}
207
208	8	return $this->goodnessOfFit;
209		}
210
211		/**
212		* Return the goodness of fit for this regression.
213		*
214		* @param int $dp Number of places of decimal precision to return
215		*
216		* @return float
217		*/
218		public function getGoodnessOfFitPercent($dp = 0)
219		{
220		if ($dp != 0) {
221		return round($this->goodnessOfFit * 100, $dp);
222		}
223
224		return $this->goodnessOfFit * 100;
225		}
226
227		/**
228		* Return the standard deviation of the residuals for this regression.
229		*
230		* @param int $dp Number of places of decimal precision to return
231		*
232		* @return float
233		*/
234	5	public function getStdevOfResiduals($dp = 0)
235		{
236	5	if ($dp != 0) {
237		return round($this->stdevOfResiduals, $dp);
238		}
239
240	5	return $this->stdevOfResiduals;
241		}
242
243		/**
244		* @param int $dp Number of places of decimal precision to return
245		*
246		* @return float
247		*/
248	3	public function getSSRegression($dp = 0)
249		{
250	3	if ($dp != 0) {
251		return round($this->SSRegression, $dp);
252		}
253
254	3	return $this->SSRegression;
255		}
256
257		/**
258		* @param int $dp Number of places of decimal precision to return
259		*
260		* @return float
261		*/
262	3	public function getSSResiduals($dp = 0)
263		{
264	3	if ($dp != 0) {
265		return round($this->SSResiduals, $dp);
266		}
267
268	3	return $this->SSResiduals;
269		}
270
271		/**
272		* @param int $dp Number of places of decimal precision to return
273		*
274		* @return float
275		*/
276	3	public function getDFResiduals($dp = 0)
277		{
278	3	if ($dp != 0) {
279		return round($this->DFResiduals, $dp);
280		}
281
282	3	return $this->DFResiduals;
283		}
284
285		/**
286		* @param int $dp Number of places of decimal precision to return
287		*
288		* @return float
289		*/
290	3	public function getF($dp = 0)
291		{
292	3	if ($dp != 0) {
293		return round($this->f, $dp);
294		}
295
296	3	return $this->f;
297		}
298
299		/**
300		* @param int $dp Number of places of decimal precision to return
301		*
302		* @return float
303		*/
304	3	public function getCovariance($dp = 0)
305		{
306	3	if ($dp != 0) {
307		return round($this->covariance, $dp);
308		}
309
310	3	return $this->covariance;
311		}
312
313		/**
314		* @param int $dp Number of places of decimal precision to return
315		*
316		* @return float
317		*/
318	2	public function getCorrelation($dp = 0)
319		{
320	2	if ($dp != 0) {
321		return round($this->correlation, $dp);
322		}
323
324	2	return $this->correlation;
325		}
326
327		/**
328		* @return float[]
329		*/
330		public function getYBestFitValues()
331		{
332		return $this->yBestFitValues;
333		}
334
335	39	protected function calculateGoodnessOfFit($sumX, $sumY, $sumX2, $sumY2, $sumXY, $meanX, $meanY, $const): void
336		{
337	39	$SSres = $SScov = $SScor = $SStot = $SSsex = 0.0;
		0 ignored issues – show Unused Code introduced 2017-12-03 09:09 UTC by Report Bug Copy Issue Report The assignment to `$SScor` is dead and can be removed. Loading history...
338	39	foreach ($this->xValues as $xKey => $xValue) {
339	39	$bestFitY = $this->yBestFitValues[$xKey] = $this->getValueOfYForX($xValue);
340
341	39	$SSres += ($this->yValues[$xKey] - $bestFitY) * ($this->yValues[$xKey] - $bestFitY);
342	39	if ($const === true) {
343	34	$SStot += ($this->yValues[$xKey] - $meanY) * ($this->yValues[$xKey] - $meanY);
344		} else {
345	5	$SStot += $this->yValues[$xKey] * $this->yValues[$xKey];
346		}
347	39	$SScov += ($this->xValues[$xKey] - $meanX) * ($this->yValues[$xKey] - $meanY);
348	39	if ($const === true) {
349	34	$SSsex += ($this->xValues[$xKey] - $meanX) * ($this->xValues[$xKey] - $meanX);
350		} else {
351	5	$SSsex += $this->xValues[$xKey] * $this->xValues[$xKey];
352		}
353		}
354
355	39	$this->SSResiduals = $SSres;
356	39	$this->DFResiduals = $this->valueCount - 1 - ($const === true ? 1 : 0);
357
358	39	if ($this->DFResiduals == 0.0) {
359	1	$this->stdevOfResiduals = 0.0;
360		} else {
361	38	$this->stdevOfResiduals = sqrt($SSres / $this->DFResiduals);
362		}
363	39	if (($SStot == 0.0) \|\| ($SSres == $SStot)) {
		1 ignored issue – show introduced 2018-10-21 04:55 UTC by Report Bug Copy Issue Report The condition `$SStot == 0.0` is always `true`. Loading history...
364		$this->goodnessOfFit = 1;
365		} else {
366	39	$this->goodnessOfFit = 1 - ($SSres / $SStot);
367		}
368
369	39	$this->SSRegression = $this->goodnessOfFit * $SStot;
370	39	$this->covariance = $SScov / $this->valueCount;
371	39	$this->correlation = ($this->valueCount * $sumXY - $sumX * $sumY) / sqrt(($this->valueCount * $sumX2 - $sumX ** 2) * ($this->valueCount * $sumY2 - $sumY ** 2));
372	39	$this->slopeSE = $this->stdevOfResiduals / sqrt($SSsex);
373	39	$this->intersectSE = $this->stdevOfResiduals * sqrt(1 / ($this->valueCount - ($sumX * $sumX) / $sumX2));
374	39	if ($this->SSResiduals != 0.0) {
375	27	if ($this->DFResiduals == 0.0) {
376		$this->f = 0.0;
377		} else {
378	27	$this->f = $this->SSRegression / ($this->SSResiduals / $this->DFResiduals);
379		}
380		} else {
381	12	if ($this->DFResiduals == 0.0) {
382	1	$this->f = 0.0;
383		} else {
384	11	$this->f = $this->SSRegression / $this->DFResiduals;
385		}
386		}
387	39	}
388
389	39	private function sumSquares(array $values)
390		{
391	39	return array_sum(
392	39	array_map(
393		function ($value) {
394	39	return $value ** 2;
395	39	},
396		$values
397		)
398		);
399		}
400
401		/**
402		* @param float[] $yValues
403		* @param float[] $xValues
404		*/
405	39	protected function leastSquareFit(array $yValues, array $xValues, bool $const): void
406		{
407		// calculate sums
408	39	$sumValuesX = array_sum($xValues);
409	39	$sumValuesY = array_sum($yValues);
410	39	$meanValueX = $sumValuesX / $this->valueCount;
411	39	$meanValueY = $sumValuesY / $this->valueCount;
412	39	$sumSquaresX = $this->sumSquares($xValues);
413	39	$sumSquaresY = $this->sumSquares($yValues);
414	39	$mBase = $mDivisor = 0.0;
415	39	$xy_sum = 0.0;
416	39	for ($i = 0; $i < $this->valueCount; ++$i) {
417	39	$xy_sum += $xValues[$i] * $yValues[$i];
418
419	39	if ($const === true) {
420	34	$mBase += ($xValues[$i] - $meanValueX) * ($yValues[$i] - $meanValueY);
421	34	$mDivisor += ($xValues[$i] - $meanValueX) * ($xValues[$i] - $meanValueX);
422		} else {
423	5	$mBase += $xValues[$i] * $yValues[$i];
424	5	$mDivisor += $xValues[$i] * $xValues[$i];
425		}
426		}
427
428		// calculate slope
429	39	$this->slope = $mBase / $mDivisor;
430
431		// calculate intersect
432	39	$this->intersect = ($const === true) ? $meanValueY - ($this->slope * $meanValueX) : 0.0;
433
434	39	$this->calculateGoodnessOfFit($sumValuesX, $sumValuesY, $sumSquaresX, $sumSquaresY, $xy_sum, $meanValueX, $meanValueY, $const);
435	39	}
436
437		/**
438		* Define the regression.
439		*
440		* @param float[] $yValues The set of Y-values for this regression
441		* @param float[] $xValues The set of X-values for this regression
442		*/
443	39	public function __construct($yValues, $xValues = [])
444		{
445		// Calculate number of points
446	39	$yValueCount = count($yValues);
447	39	$xValueCount = count($xValues);
448
449		// Define X Values if necessary
450	39	if ($xValueCount === 0) {
451		$xValues = range(1, $yValueCount);
452	39	} elseif ($yValueCount !== $xValueCount) {
453		// Ensure both arrays of points are the same size
454		$this->error = true;
455		}
456
457	39	$this->valueCount = $yValueCount;
458	39	$this->xValues = $xValues;
459	39	$this->yValues = $yValues;
460	39	}
461		}
462

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BestFit::calculateGoodnessOfFit() C

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