$error
$error : bool
Indicator flag for a calculation error.
$error : bool
Indicator flag for a calculation error.
$bestFitType : string
Algorithm type to use for best-fit (Name of this Trend class).
$valueCount : int
Number of entries in the sets of x- and y-value arrays.
$xValues : float[]
X-value dataseries of values.
$yValues : float[]
Y-value dataseries of values.
$adjustToZero : bool
Flag indicating whether values should be adjusted to Y=0.
$yBestFitValues : float[]
Y-value series of best-fit values.
$order : int
Polynomial order.
__construct(int $order, float[] $yValues, float[] $xValues = [], bool $const = true) : mixed
Define the regression and calculate the goodness of fit for a set of X and Y data values.
| int | $order | Order of Polynomial for this regression |
| float[] | $yValues | The set of Y-values for this regression |
| float[] | $xValues | The set of X-values for this regression |
| bool | $const |
polynomialRegression(int $order, float[] $yValues, float[] $xValues) : void
Execute the regression and calculate the goodness of fit for a set of X and Y data values.
| int | $order | Order of Polynomial for this regression |
| float[] | $yValues | The set of Y-values for this regression |
| float[] | $xValues | The set of X-values for this regression |