SEB Linear Regression Slope

/*********************************Provided By:      eSignal (Copyright c eSignal), a division of Interactive Data     Corporation. 2009. All rights reserved. This sample eSignal     Formula Script (EFS) is for educational purposes only and may be     modified and saved under a new file name.  eSignal is not responsible    for the functionality once modified.  eSignal reserves the right     to modify and overwrite this EFS file with each new release.Description:            This indicator plots the slope of linear regression in osccilator-like     manner. This is a part of Standard Error Bands study. Version:            1.0  03/19/2009Formula Parameters:                     Default:    Length                              21 Notes:    Linear Regression is a concept also know as the "least squares method" or     "best fit". Linear Regression attempts to fit a straight line between several     data points in such a way that distance between each data point and the line is     minimized.    "Standard Error Bands" Jon Anderson, Stocks&Commodities Magazine, Traders Tips, 09/1996 **********************************/var fpArray = new Array();var bInit = false;function preMain() {    setStudyTitle("SEB LinRegSlope");    setCursorLabelName("Slope", 0);    setDefaultBarFgColor(Color.red, 0);    addBand(0, PS_SOLID, 1, Color.cyan);    var x=0;    fpArray[x] = new FunctionParameter("Length", FunctionParameter.NUMBER);	with(fpArray[x++]){        setLowerLimit(1);		        setDefault(21);    }}var xSL = null;function main(Length) {var nBarState = getBarState();var nSL = 0;    if (nBarState == BARSTATE_ALLBARS) {        if (Length == null) Length = 21;    }        if (bInit == false) {        xSL = efsInternal("calc", Length);        bInit = true;    }    nSL = xSL.getValue(0);    if (nSL == null) return;    return nSL;}var xClose = null;var xInit = false;function calc(Length) {var SL = 0;var SumBars = Length * (Length - 1) * 0.5;var SumSqrBars = (Length - 1) * Length * (2 * Length - 1) / 6;var Sum1 = 0;var SumY = 0;var i = 0;    if(xInit==false){        xClose = close();        xInit = true;    }    for (i = 0; i < Length; i++) {        Sum1 += i * xClose.getValue(-i);        SumY += xClose.getValue(-i);    }    var Sum2 = SumBars * SumY;    var Num1 = Length * Sum1 - Sum2;    var Num2 = SumBars * SumBars - Length * SumSqrBars;    SL = Num1 / Num2;    return SL;}