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diff --git a/helpcontent2/source/text/schart/01/04050100.xhp b/helpcontent2/source/text/schart/01/04050100.xhp index 37a65ed1d8..aec3873dde 100644 --- a/helpcontent2/source/text/schart/01/04050100.xhp +++ b/helpcontent2/source/text/schart/01/04050100.xhp @@ -1,17 +1,14 @@ <?xml version="1.0" encoding="UTF-8"?> <helpdocument version="1.0"> - + <!-- *********************************************************************** * * DO NOT ALTER OR REMOVE COPYRIGHT NOTICES OR THIS FILE HEADER. - * - * Copyright 2008 by Sun Microsystems, Inc. * - * OpenOffice.org - a multi-platform office productivity suite + * Copyright 2000, 2010 Oracle and/or its affiliates. * - * $RCSfile: soffice2xmlhelp.xsl,v $ - * $Revision: 1.8 $ + * OpenOffice.org - a multi-platform office productivity suite * * This file is part of OpenOffice.org. * @@ -32,8 +29,8 @@ * ************************************************************************ --> - - + + <meta> <topic id="textschart0104050100xml" indexer="include" status="PUBLISH"> <title xml-lang="en-US" id="tit">Trend Lines </title> @@ -93,23 +90,23 @@ <paragraph xml-lang="en-US" id="par_id846888" role="paragraph" l10n="NEW"><ahelp hid=".">To show the trend line equation, select the trend line in the chart, right-click to open the context menu, and choose <emph>Insert Trend Line Equation</emph>.</ahelp></paragraph> <paragraph xml-lang="en-US" id="par_id8962065" role="paragraph" l10n="CHG">When the chart is in edit mode, %PRODUCTNAME gives you the equation of the trend line and the coefficient of determination R². Click on the trend line to see the information in the status bar.</paragraph> <paragraph xml-lang="en-US" id="par_id1328470" role="note" l10n="NEW">For a category chart (for example a line chart), the regression information is calculated using numbers 1, 2, 3, … as x-values. This is also true if your data series uses other numbers as names for the x-values. For such charts the XY chart type might be more suitable.</paragraph> - <paragraph xml-lang="en-US" id="par_id8092593" role="paragraph" l10n="CHG">To show the equation and the coefficient of determination, select the regression curve and choose <item type="menuitem">Format - Format Selection - Equation</item>. <comment>see http://specs.openoffice.org/chart/DisplayTrendLineEquations.odt</comment></paragraph><comment>hid</comment> + <paragraph xml-lang="en-US" id="par_id8092593" role="paragraph" l10n="CHG">To show the equation and the coefficient of determination, select the regression curve and choose <item type="menuitem">Format - Format Selection - Equation</item>.</paragraph> <paragraph xml-lang="en-US" id="par_id7971434" role="paragraph" l10n="NEW"><ahelp hid="." visibility="hidden">Enable Show equation to see the equation of the regression curve.</ahelp></paragraph><comment>hid</comment> <paragraph xml-lang="en-US" id="par_id558793" role="paragraph" l10n="CHG"><ahelp hid="." visibility="hidden">Enable Show Coefficient of Determination to see the determination coefficient of the regression curve.</ahelp></paragraph> <paragraph xml-lang="en-US" id="par_id7735221" role="paragraph" l10n="NEW">You can also calculate the parameters using Calc functions as follows.</paragraph> - <paragraph xml-lang="en-US" id="hd_id5744193" role="heading" level="1" l10n="NEW">The linear regression equation</paragraph> + <paragraph xml-lang="en-US" id="hd_id5744193" role="heading" level="2" l10n="NEW">The linear regression equation</paragraph> <paragraph xml-lang="en-US" id="par_id9251991" role="paragraph" l10n="NEW">The <emph>linear regression</emph> follows the equation <item type="literal">y=m*x+b</item>.</paragraph> <paragraph xml-lang="en-US" id="par_id7951902" role="code" l10n="NEW">m = SLOPE(Data_Y;Data_X) </paragraph> <paragraph xml-lang="en-US" id="par_id6637165" role="code" l10n="NEW">b = INTERCEPT(Data_Y ;Data_X) </paragraph> <paragraph xml-lang="en-US" id="par_id7879268" role="paragraph" l10n="NEW">Calculate the coefficient of determination by</paragraph> <paragraph xml-lang="en-US" id="par_id9244361" role="code" l10n="NEW">r² = RSQ(Data_Y;Data_X) </paragraph> <paragraph xml-lang="en-US" id="par_id2083498" role="paragraph" l10n="NEW">Besides m, b and r² the array function <emph>LINEST</emph> provides additional statistics for a regression analysis.</paragraph> - <paragraph xml-lang="en-US" id="hd_id2538834" role="heading" level="1" l10n="NEW">The logarithm regression equation</paragraph> + <paragraph xml-lang="en-US" id="hd_id2538834" role="heading" level="2" l10n="NEW">The logarithm regression equation</paragraph> <paragraph xml-lang="en-US" id="par_id394299" role="paragraph" l10n="NEW">The <emph>logarithm regression</emph> follows the equation <item type="literal">y=a*ln(x)+b</item>.</paragraph> <paragraph xml-lang="en-US" id="par_id2134159" role="code" l10n="NEW">a = SLOPE(Data_Y;LN(Data_X)) </paragraph> <paragraph xml-lang="en-US" id="par_id5946531" role="code" l10n="NEW">b = INTERCEPT(Data_Y ;LN(Data_X)) </paragraph> <paragraph xml-lang="en-US" id="par_id5649281" role="code" l10n="NEW">r² = RSQ(Data_Y;LN(Data_X)) </paragraph> - <paragraph xml-lang="en-US" id="hd_id7874080" role="heading" level="1" l10n="NEW">The exponential regression equation</paragraph> + <paragraph xml-lang="en-US" id="hd_id7874080" role="heading" level="2" l10n="NEW">The exponential regression equation</paragraph> <paragraph xml-lang="en-US" id="par_id4679097" role="paragraph" l10n="NEW"> For exponential regression curves a transformation to a linear model takes place. The optimal curve fitting is related to the linear model and the results are interpreted accordingly. </paragraph> <paragraph xml-lang="en-US" id="par_id9112216" role="paragraph" l10n="NEW">The exponential regression follows the equation <item type="literal">y=b*exp(a*x)</item> or <item type="literal">y=b*m^x</item>, which is transformed to <item type="literal">ln(y)=ln(b)+a*x</item> or <item type="literal">ln(y)=ln(b)+ln(m)*x</item> respectively.</paragraph> <paragraph xml-lang="en-US" id="par_id4416638" role="code" l10n="NEW">a = SLOPE(LN(Data_Y);Data_X) </paragraph> @@ -119,12 +116,12 @@ <paragraph xml-lang="en-US" id="par_id7127292" role="paragraph" l10n="NEW">Calculate the coefficient of determination by</paragraph> <paragraph xml-lang="en-US" id="par_id5437177" role="code" l10n="NEW">r² = RSQ(LN(Data_Y);Data_X) </paragraph> <paragraph xml-lang="en-US" id="par_id6946317" role="paragraph" l10n="NEW">Besides m, b and r² the array function LOGEST provides additional statistics for a regression analysis.</paragraph> - <paragraph xml-lang="en-US" id="hd_id6349375" role="heading" level="1" l10n="NEW">The power regression equation</paragraph> + <paragraph xml-lang="en-US" id="hd_id6349375" role="heading" level="2" l10n="NEW">The power regression equation</paragraph> <paragraph xml-lang="en-US" id="par_id1857661" role="paragraph" l10n="NEW"> For <emph>power regression</emph> curves a transformation to a linear model takes place. The power regression follows the equation <item type="literal">y=b*x^a</item> , which is transformed to <item type="literal">ln(y)=ln(b)+a*ln(x)</item>.</paragraph> <paragraph xml-lang="en-US" id="par_id8517105" role="code" l10n="NEW">a = SLOPE(LN(Data_Y);LN(Data_X)) </paragraph> <paragraph xml-lang="en-US" id="par_id9827265" role="code" l10n="NEW">b = EXP(INTERCEPT(LN(Data_Y);LN(Data_X)) </paragraph> <paragraph xml-lang="en-US" id="par_id2357249" role="code" l10n="NEW">r² = RSQ(LN(Data_Y);LN(Data_X)) </paragraph> - <paragraph xml-lang="en-US" id="hd_id9204077" role="heading" level="1" l10n="NEW">Constraints<comment>UFI: is this still so?</comment></paragraph> + <paragraph xml-lang="en-US" id="hd_id9204077" role="heading" level="2" l10n="NEW">Constraints<comment>UFI: is this still so?</comment></paragraph> <paragraph xml-lang="en-US" id="par_id7393719" role="paragraph" l10n="CHG"> The calculation of the trend line considers only data pairs with the following values:</paragraph> <list type="ordered"> <listitem> @@ -138,7 +135,7 @@ </listitem> </list> <paragraph xml-lang="en-US" id="par_id181279" role="paragraph" l10n="NEW">You should transform your data accordingly; it is best to work on a copy of the original data and transform the copied data.</paragraph> - <paragraph xml-lang="en-US" id="hd_id7907040" role="heading" level="1" l10n="NEW">The polynomial regression equation</paragraph> + <paragraph xml-lang="en-US" id="hd_id7907040" role="heading" level="2" l10n="NEW">The polynomial regression equation</paragraph> <paragraph xml-lang="en-US" id="par_id8918729" role="paragraph" l10n="NEW">A <emph>polynomial regression</emph> curve cannot be added automatically. You must calculate this curve manually. </paragraph> <paragraph xml-lang="en-US" id="par_id33875" role="paragraph" l10n="NEW">Create a table with the columns x, x², x³, … , xⁿ, y up to the desired degree n. </paragraph> <paragraph xml-lang="en-US" id="par_id8720053" role="paragraph" l10n="NEW">Use the formula <item type="literal">=LINEST(Data_Y,Data_X)</item> with the complete range x to xⁿ (without headings) as Data_X. </paragraph> |