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Defines 1 class

PHPExcel_Power_Best_Fit:: (6 methods):
  getValueOfYForX()
  getValueOfXForY()
  getEquation()
  getIntersect()
  powerRegression()
  __construct()


Class: PHPExcel_Power_Best_Fit  - X-Ref

PHPExcel_Power_Best_Fit

Copyright (c) 2006 - 2015 PHPExcel

This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.

This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
Lesser General Public License for more details.

You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301  USA

getValueOfYForX($xValue)   X-Ref
Return the Y-Value for a specified value of X

param: float        $xValue            X-Value
return: float                        Y-Value

getValueOfXForY($yValue)   X-Ref
Return the X-Value for a specified value of Y

param: float        $yValue            Y-Value
return: float                        X-Value

getEquation($dp = 0)   X-Ref
Return the Equation of the best-fit line

param: int        $dp        Number of places of decimal precision to display
return: string

getIntersect($dp = 0)   X-Ref
Return the Value of X where it intersects Y = 0

param: int        $dp        Number of places of decimal precision to display
return: string

powerRegression($yValues, $xValues, $const)   X-Ref
Execute the regression and calculate the goodness of fit for a set of X and Y data values

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

__construct($yValues, $xValues = array()   X-Ref
Define the regression and calculate the goodness of fit for a set of X and Y data values

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



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