getError()
getBestFitType()
getValueOfYForX()
getValueOfXForY()
getXValues()
getEquation()
getSlope()
getSlopeSE()
getIntersect()
getIntersectSE()
getGoodnessOfFit()
getGoodnessOfFitPercent()
getStdevOfResiduals()
getSSRegression()
getSSResiduals()
getDFResiduals()
getF()
getCovariance()
getCorrelation()
getYBestFitValues()
__construct()

No public properties found

No constants found

calculateGoodnessOfFit()
leastSquareFit()

$error
$bestFitType
$valueCount
$xValues
$yValues
$adjustToZero
$yBestFitValues
$goodnessOfFit
$stdevOfResiduals
$covariance
$correlation
$SSRegression
$SSResiduals
$DFResiduals
$f
$slope
$slopeSE
$intersect
$intersectSE
$xOffset
$yOffset

N/A

No private methods found

No private properties found

N/A

File: serve/vendor/phpoffice/phpspreadsheet/src/PhpSpreadsheet/Shared/Trend/BestFit.php
Package: Application
Class hierarchy: \PhpOffice\PhpSpreadsheet\Shared\Trend\BestFit
Uses

Properties

$error

$error : bool

Indicator flag for a calculation error.

Type

bool —

$bestFitType

$bestFitType : string

Algorithm type to use for best-fit.

Type

string —

$valueCount

$valueCount : int

Number of entries in the sets of x- and y-value arrays.

Type

int —

$xValues

$xValues : float[]

X-value dataseries of values.

Type

float[] —

$yValues

$yValues : float[]

Y-value dataseries of values.

Type

float[] —

$adjustToZero

$adjustToZero : bool

Flag indicating whether values should be adjusted to Y=0.

Type

bool —

$yBestFitValues

$yBestFitValues : float[]

Y-value series of best-fit values.

Type

float[] —

$goodnessOfFit

$goodnessOfFit

$stdevOfResiduals

$stdevOfResiduals

$covariance

$covariance

$correlation

$correlation

$SSRegression

$SSRegression

$SSResiduals

$SSResiduals

$DFResiduals

$DFResiduals

$f

$f

$slope

$slope

$slopeSE

$slopeSE

$intersect

$intersect

$intersectSE

$intersectSE

$xOffset

$xOffset

$yOffset

$yOffset

Methods

getError()

getError() : mixed

Returns

mixed —

getBestFitType()

getBestFitType() : mixed

Returns

mixed —

getValueOfYForX()

getValueOfYForX(float  $xValue) : bool

Return the Y-Value for a specified value of X.

Parameters

float

$xValue

X-Value

Returns

bool —

Y-Value

getValueOfXForY()

getValueOfXForY(float  $yValue) : bool

Return the X-Value for a specified value of Y.

Parameters

float

$yValue

Y-Value

Returns

bool —

X-Value

getXValues()

getXValues() : float[]

Return the original set of X-Values.

Returns

float[] —

X-Values

getEquation()

getEquation(int  $dp) : bool

Return the Equation of the best-fit line.

Parameters

int

$dp

Number of places of decimal precision to display

Returns

bool —

getSlope()

getSlope(int  $dp) : float

Return the Slope of the line.

Parameters

int

$dp

Number of places of decimal precision to display

Returns

float —

getSlopeSE()

getSlopeSE(int  $dp) : float

Return the standard error of the Slope.

Parameters

int

$dp

Number of places of decimal precision to display

Returns

float —

getIntersect()

getIntersect(int  $dp) : float

Return the Value of X where it intersects Y = 0.

Parameters

int

$dp

Number of places of decimal precision to display

Returns

float —

getIntersectSE()

getIntersectSE(int  $dp) : float

Return the standard error of the Intersect.

Parameters

int

$dp

Number of places of decimal precision to display

Returns

float —

getGoodnessOfFit()

getGoodnessOfFit(int  $dp) : float

Return the goodness of fit for this regression.

Parameters

int

$dp

Number of places of decimal precision to return

Returns

float —

getGoodnessOfFitPercent()

getGoodnessOfFitPercent(int  $dp) : float

Return the goodness of fit for this regression.

Parameters

int

$dp

Number of places of decimal precision to return

Returns

float —

getStdevOfResiduals()

getStdevOfResiduals(int  $dp) : float

getYBestFitValues() : float[]

Returns

float[] —

__construct()

__construct(float[]  $yValues, float[]  $xValues = [], bool  $const = true) : mixed

Define the regression.

Parameters

float[]	$yValues	The set of Y-values for this regression
float[]	$xValues	The set of X-values for this regression
bool	$const

Returns

mixed —

calculateGoodnessOfFit()

calculateGoodnessOfFit(mixed  $sumX, mixed  $sumY, mixed  $sumX2, mixed  $sumY2, mixed  $sumXY, mixed  $meanX, mixed  $meanY, mixed  $const) : void

Parameters

mixed	$sumX
mixed	$sumY
mixed	$sumX2
mixed	$sumY2
mixed	$sumXY
mixed	$meanX
mixed	$meanY
mixed	$const

leastSquareFit()

leastSquareFit(float[]  $yValues, float[]  $xValues, bool  $const) : void

Parameters

float[]	$yValues
float[]	$xValues
bool	$const