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| author | Stanislav Horacek <stanislav.horacek@gmail.com> | 2016-07-24 16:44:32 +0200 |
|---|---|---|
| committer | jan iversen <jani@documentfoundation.org> | 2016-07-25 05:18:35 +0000 |
| commit | 8e6440c88f34ad595feca16d0cffbf14940d2fd8 (patch) | |
| tree | 54f8c9bed2c3be6c00540fe52437727fd651aef2 | |
| parent | FORECAST.ETS.* confidence level: use precise argument name (diff) | |
| download | help-8e6440c88f34ad595feca16d0cffbf14940d2fd8.tar.gz help-8e6440c88f34ad595feca16d0cffbf14940d2fd8.zip | |
fix spelling of "coefficient"
Change-Id: Id988f9127d8d7af2dbd72ec899168d21b7d2923b
Reviewed-on: https://gerrit.libreoffice.org/27481
Reviewed-by: jan iversen <jani@documentfoundation.org>
Tested-by: jan iversen <jani@documentfoundation.org>
| -rw-r--r-- | source/text/scalc/01/statistics_regression.xhp | 2 |
1 files changed, 1 insertions, 1 deletions
diff --git a/source/text/scalc/01/statistics_regression.xhp b/source/text/scalc/01/statistics_regression.xhp index 658feb2d2a..39ddea70b4 100644 --- a/source/text/scalc/01/statistics_regression.xhp +++ b/source/text/scalc/01/statistics_regression.xhp @@ -58,7 +58,7 @@ <paragraph id="par_id1701201620340168" role="ul_item" xml-lang="en-US"><emph>Logarithmic regression</emph>: find a logarithmic curve in the form of <item type="literal">y = a.ln(x) + b</item>, where <item type="literal">a</item> is the slope, <item type="literal">b</item> is the intercept and <item type="literal">ln(x)</item> is the natural logarithm of <item type="literal">x</item>, that best fits the data.</paragraph> </listitem> <listitem> - <paragraph id="par_id1701201620340139" role="ul_item" xml-lang="en-US"><emph>Power regression</emph>: Find a power curve in the form of <item type="literal">y = a.x^b</item>, where <item type="literal">a</item> is the coeficient, <item type="literal">b</item> is the power that best fits the data.</paragraph> + <paragraph id="par_id1701201620340139" role="ul_item" xml-lang="en-US"><emph>Power regression</emph>: Find a power curve in the form of <item type="literal">y = a.x^b</item>, where <item type="literal">a</item> is the coefficient, <item type="literal">b</item> is the power that best fits the data.</paragraph> </listitem></list> <embed href="text/scalc/01/stat_data.xhp#regressiondata"/> <paragraph id="par_id1001310" role="paragraph" xml-lang="en-US">The results of the three types of <emph>regression analysis</emph> of the measurements in the table above are shown below.</paragraph> |