source/text/scalc/01/func_forecastetsadd.xhp | 2 +-
source/text/scalc/01/func_forecastetsmult.xhp | 2 +-
source/text/scalc/01/func_forecastetspiadd.xhp | 10 +++++-----
source/text/scalc/01/func_forecastetspimult.xhp | 10 +++++-----
source/text/scalc/01/statistics_regression.xhp | 2 +-
5 files changed, 13 insertions(+), 13 deletions(-)
New commits:
commit 16c021b2259e92fd21da3689309375fcc21891b2
Author: Olivier Hallot <ohal...@collabora.co.uk>
Date: Fri Jun 17 17:03:15 2016 -0300
Better wording for FORECAST.ETS.* help pages
Change-Id: Iac41e281088fb1ca48f1b5509d38301405ff6348
Reviewed-on: https://gerrit.libreoffice.org/26437
Reviewed-by: Olivier Hallot <ohal...@collabora.co.uk>
Tested-by: Olivier Hallot <ohal...@collabora.co.uk>
diff --git a/source/text/scalc/01/func_forecastetsadd.xhp
b/source/text/scalc/01/func_forecastetsadd.xhp
index 3bd1b12..0209c75 100644
--- a/source/text/scalc/01/func_forecastetsadd.xhp
+++ b/source/text/scalc/01/func_forecastetsadd.xhp
@@ -24,7 +24,7 @@
</bookmark>
<paragraph id="hd_id0603201610022291" role="heading" level="1"
xml-lang="en-US"><link
href="text/scalc/01/func_forecastetsadd.xhp">FORECAST.ETS.ADD
function</link></paragraph>
-<paragraph id="par_id0603201610023949" role="paragraph"
xml-lang="en-US"><ahelp hid="HID_FUNC_FORECAST_ETS_ADD">Calculates forecast(s)
(future values) based on the historical data using ETS or EDS
algorithms</ahelp>. EDS is used when argument <emph>period_length</emph> is 0,
otherwise ETS is used.</paragraph>
+<paragraph id="par_id0603201610023949" role="paragraph"
xml-lang="en-US"><ahelp hid="HID_FUNC_FORECAST_ETS_ADD">Calculates the additive
forecast(s) (future values) based on the historical data using ETS or EDS
algorithms</ahelp>. EDS is used when argument <emph>period_length</emph> is 0,
otherwise ETS is used.</paragraph>
<embed href="text/scalc/01/exponsmooth_embd.xhp#intro"/>
<paragraph id="par_id0603201608440530" role="paragraph"
xml-lang="en-US">FORECAST.ETS.ADD calculates with the model</paragraph>
<embed href="text/scalc/01/exponsmooth_embd.xhp#etsadd"/>
diff --git a/source/text/scalc/01/func_forecastetsmult.xhp
b/source/text/scalc/01/func_forecastetsmult.xhp
index 21efa04..5439f04 100644
--- a/source/text/scalc/01/func_forecastetsmult.xhp
+++ b/source/text/scalc/01/func_forecastetsmult.xhp
@@ -25,7 +25,7 @@
<paragraph id="hd_id0603201610022291" role="heading" level="1"
xml-lang="en-US"><link href="text/scalc/01/func_forecastetsmult.xhp">
FORECAST.ETS.MULT Function</link></paragraph>
-<paragraph id="par_id0603201610023949" role="paragraph"
xml-lang="en-US"><ahelp hid="HID_FUNC_FORECAST_ETS_MULT">Calculates additive
forecast(s) (future values) based on the historical data using ETS or EDS
algorithms</ahelp>. EDS is used when argument <emph>period_length</emph> is 0,
otherwise ETS is used.</paragraph>
+<paragraph id="par_id0603201610023949" role="paragraph"
xml-lang="en-US"><ahelp hid="HID_FUNC_FORECAST_ETS_MULT">Calculates the
multiplicative forecast(s) (future values) based on the historical data using
ETS or EDS algorithms</ahelp>. EDS is used when argument
<emph>period_length</emph> is 0, otherwise ETS is used.</paragraph>
<embed href="text/scalc/01/exponsmooth_embd.xhp#intro"/>
<paragraph id="par_id0603201608440530" role="paragraph"
xml-lang="en-US">FORECAST.ETS.MULT calculates with the model </paragraph>
<embed href="text/scalc/01/exponsmooth_embd.xhp#etsmult"/>
diff --git a/source/text/scalc/01/func_forecastetspiadd.xhp
b/source/text/scalc/01/func_forecastetspiadd.xhp
index 2178035..cfcdc01 100644
--- a/source/text/scalc/01/func_forecastetspiadd.xhp
+++ b/source/text/scalc/01/func_forecastetspiadd.xhp
@@ -25,7 +25,7 @@
</bookmark>
<paragraph id="hd_id0603201617134175" role="heading" level="1"
xml-lang="en-US"><link
href="text/scalc/01/func_forecastetspiadd.xhp">FORECAST.ETS.PI.ADD
function</link></paragraph>
-<paragraph id="par_id0603201617141750" role="paragraph"
xml-lang="en-US"><ahelp hid="HID_FUNC_FORECAST_ETS_PIA">Calculates Prediction
Interval(s) based on the historical data using ETS or EDS algorithms.</ahelp>.
EDS is used when argument <emph>period_length</emph> is 0, otherwise ETS is
used.</paragraph>
+<paragraph id="par_id0603201617141750" role="paragraph"
xml-lang="en-US"><ahelp hid="HID_FUNC_FORECAST_ETS_PIA">Calculates the
prediction interval(s) for additive forecast based on the historical data using
ETS or EDS algorithms.</ahelp>. EDS is used when argument
<emph>period_length</emph> is 0, otherwise ETS is used.</paragraph>
<embed href="text/scalc/01/exponsmooth_embd.xhp#intro"/>
<paragraph id="par_id0603201610005998" role="paragraph"
xml-lang="en-US">FORECAST.ETS.PI.ADD calculates with the model</paragraph>
<embed href="text/scalc/01/exponsmooth_embd.xhp#etsadd"/>
@@ -40,14 +40,14 @@
<embed href="text/scalc/01/exponsmooth_embd.xhp#datacompletion"/>
<embed href="text/scalc/01/exponsmooth_embd.xhp#aggregation"/>
<paragraph id="par_id0403201618595126" role="paragraph" xml-lang="en-US">For
example, with a 90% Confidence level, a 90% prediction interval will be
computed (90% of future points are to fall within this radius from forecast).
</paragraph>
-<paragraph id="par_id0403201618595143" role="note" xml-lang="en-US">Note on
Prediction Intervals: there is no exact mathematical way to calculate this for
forecasts, there are various approximations. Prediction Intervals tend to be
increasingly 'over-optimistic' with increasing distance of the forecast-X to
the observation data set.</paragraph>
+<paragraph id="par_id0403201618595143" role="note" xml-lang="en-US">Note on
prediction intervals: there is no exact mathematical way to calculate this for
forecasts, there are various approximations. Prediction intervals tend to be
increasingly 'over-optimistic' when increasing distance of the forecast-X from
the observation data set.</paragraph>
<paragraph id="par_id0403201618595150" role="paragraph" xml-lang="en-US">For
ETS, Calc uses an approximation based on 1000 calculations with random
variations within the standard deviation of the observation data set (the
historical values).</paragraph>
<embed href="text/scalc/01/exponsmooth_embd.xhp#exampledata"/>
- <paragraph id="hd_id04032016185123" role="code"
xml-lang="en-US">=FORECAST.ETS.PI.ADD(DATE(2014;1;1);Values;Timeline;0,9;1;TRUE();1)</paragraph>
- <paragraph id="hd_id04032016112394554" role="paragraph"
xml-lang="en-US">Returns 18.8061295551355, the additive prediction interval
forecast for January 2014 based on <emph>Values</emph> and
<emph>Timeline</emph> named ranges above, with one sample per period, no
missing data, and AVERAGE as aggregation.</paragraph>
+ <paragraph id="hd_id04032016185123" role="code"
xml-lang="en-US">=FORECAST.ETS.PI.ADD(DATE(2014;1;1);Values;Timeline;0.9;1;TRUE();1)</paragraph>
+ <paragraph id="hd_id04032016112394554" role="paragraph"
xml-lang="en-US">Returns 18.8061295551355, the prediction interval for additive
forecast for January 2014 based on <emph>Values</emph> and
<emph>Timeline</emph> named ranges above, 90% (=0.9) confidence level, with one
sample per period, no missing data, and AVERAGE as aggregation.</paragraph>
<paragraph id="hd_id04032123185123" role="code"
xml-lang="en-US">=FORECAST.ETS.PI.ADD(DATE(2014;1;1);Values;Timeline;0.8;4;TRUE();7)</paragraph>
- <paragraph id="hd_id040312316112394554" role="paragraph"
xml-lang="en-US">Returns 23.4416821953741, the additive prediction interval
forecast for January 2014 based on <emph>Values</emph> and
<emph>Timeline</emph> named ranges above, with confidence level of 0.8, period
length of 4, no missing data, and SUM as aggregation.</paragraph>
+ <paragraph id="hd_id040312316112394554" role="paragraph"
xml-lang="en-US">Returns 23.4416821953741, the prediction interval for additive
forecast for January 2014 based on <emph>Values</emph> and
<emph>Timeline</emph> named ranges above, with confidence level of 0.8, period
length of 4, no missing data, and SUM as aggregation.</paragraph>
</section>
<section id="relatedtopics">
<paragraph id="par_id0603201619261276" role="paragraph" xml-lang="en-US">See
also :
diff --git a/source/text/scalc/01/func_forecastetspimult.xhp
b/source/text/scalc/01/func_forecastetspimult.xhp
index 534c9b3..882c4c8 100644
--- a/source/text/scalc/01/func_forecastetspimult.xhp
+++ b/source/text/scalc/01/func_forecastetspimult.xhp
@@ -25,7 +25,7 @@
<paragraph id="hd_id0603201617134175" role="heading" level="1"
xml-lang="en-US"><link
href="text/scalc/01/func_forecastetspimult.xhp">FORECAST.ETS.PI.MULT
function</link></paragraph>
-<paragraph id="par_id0603201617141750" role="paragraph"
xml-lang="en-US"><ahelp hid="HID_FUNC_FORECAST_ETS_PIM">Calculates Prediction
Interval(s) based on the historical data using ETS or EDS algorithms.</ahelp>.
EDS is used when argument <emph>period_length</emph> is 0, otherwise ETS is
used.</paragraph>
+<paragraph id="par_id0603201617141750" role="paragraph"
xml-lang="en-US"><ahelp hid="HID_FUNC_FORECAST_ETS_PIM">Calculates the
prediction interval(s) for multiplicative forecast based on the historical data
using ETS or EDS algorithms.</ahelp>. EDS is used when argument
<emph>period_length</emph> is 0, otherwise ETS is used.</paragraph>
<embed href="text/scalc/01/exponsmooth_embd.xhp#intro"/>
<paragraph id="par_id0603201610005998" role="paragraph"
xml-lang="en-US">FORECAST.ETS.PI.MULT calculates with the model</paragraph>
<embed href="text/scalc/01/exponsmooth_embd.xhp#etsmult"/>
@@ -40,14 +40,14 @@
<embed href="text/scalc/01/exponsmooth_embd.xhp#datacompletion"/>
<embed href="text/scalc/01/exponsmooth_embd.xhp#aggregation"/>
<paragraph id="par_id0403201618595126" role="paragraph" xml-lang="en-US">For
example, with a 90% Confidence level, a 90% prediction interval will be
computed (90% of future points are to fall within this radius from forecast).
</paragraph>
-<paragraph id="par_id0403201618595143" role="note" xml-lang="en-US">Note on
Prediction Intervals: there is no exact mathematical way to calculate this for
forecasts, there are various approximations. Prediction Intervals tend to be
increasingly 'over-optimistic' with increasing distance of the forecast-X to
the observation data set.</paragraph>
+<paragraph id="par_id0403201618595143" role="note" xml-lang="en-US">Note on
prediction intervals: there is no exact mathematical way to calculate this for
forecasts, there are various approximations. Prediction intervals tend to be
increasingly 'over-optimistic' when increasing distance of the forecast-X from
the observation data set.</paragraph>
<paragraph id="par_id0403201618595150" role="paragraph" xml-lang="en-US">For
ETS, Calc uses an approximation based on 1000 calculations with random
variations within the standard deviation of the observation data set (the
historical values).</paragraph>
<embed href="text/scalc/01/exponsmooth_embd.xhp#exampledata"/>
- <paragraph id="hd_id04032016185123" role="code"
xml-lang="en-US">=FORECAST.ETS.PI.MULT(DATE(2014;1;1);Values;Timeline;0,9;1;TRUE();1)</paragraph>
- <paragraph id="hd_id04032016112394554" role="paragraph"
xml-lang="en-US">Returns 20.1040952101013, the multiplicative prediction
interval forecast for January 2014 based on <emph>Values</emph> and
<emph>Timeline</emph> named ranges above, with one sample per period, no
missing data, and AVERAGE as aggregation.</paragraph>
+ <paragraph id="hd_id04032016185123" role="code"
xml-lang="en-US">=FORECAST.ETS.PI.MULT(DATE(2014;1;1);Values;Timeline;0.9;1;TRUE();1)</paragraph>
+ <paragraph id="hd_id04032016112394554" role="paragraph"
xml-lang="en-US">Returns 20.1040952101013, the prediction interval for
multiplicative forecast for January 2014 based on <emph>Values</emph> and
<emph>Timeline</emph> named ranges above, confidence level of 90% (=0.9) with
one sample per period, no missing data, and AVERAGE as aggregation.</paragraph>
<paragraph id="hd_id04032123185123" role="code"
xml-lang="en-US">=FORECAST.ETS.PI.MULT(DATE(2014;1;1);Values;Timeline;0.8;4;TRUE();7)</paragraph>
- <paragraph id="hd_id040312316112394554" role="paragraph"
xml-lang="en-US">Returns 27.5285874381574, the multiplicative prediction
interval forecast for January 2014 based on <emph>Values</emph> and
<emph>Timeline</emph> named ranges above, with confidence level of 0.8, period
length of 4, no missing data, and SUM as aggregation.</paragraph>
+ <paragraph id="hd_id040312316112394554" role="paragraph"
xml-lang="en-US">Returns 27.5285874381574, the prediction interval for
multiplicative forecast for January 2014 based on <emph>Values</emph> and
<emph>Timeline</emph> named ranges above, with confidence level of 0.8, period
length of 4, no missing data, and SUM as aggregation.</paragraph>
</section>
<section id="relatedtopics">
<paragraph id="par_id0603201619261276" role="paragraph" xml-lang="en-US">See
also :
diff --git a/source/text/scalc/01/statistics_regression.xhp
b/source/text/scalc/01/statistics_regression.xhp
index 26ec722..f02fee8 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 slope, <item type="literal">b</item> is the intercept 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 coeficient, <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>
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