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diff --git a/helpcontent2/source/text/schart/01/04050100.xhp b/helpcontent2/source/text/schart/01/04050100.xhp index 7e3db7fc06..aec3873dde 100644 --- a/helpcontent2/source/text/schart/01/04050100.xhp +++ b/helpcontent2/source/text/schart/01/04050100.xhp @@ -94,19 +94,19 @@ <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> @@ -116,12 +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> @@ -135,7 +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> |