From 768ebf50c5564dc4ecbde7af8dd136c4acdf87f4 Mon Sep 17 00:00:00 2001 From: Olivier Hallot Date: Sat, 22 Jul 2017 12:23:36 -0300 Subject: Fix some DTD issues in Help Pages does not have child nodes Change-Id: Ieac002b65cfc54c66af92e1a7cb80a1fc7ce31f4 Reviewed-on: https://gerrit.libreoffice.org/40313 Reviewed-by: Olivier Hallot Tested-by: Olivier Hallot --- source/text/scalc/guide/autofilter.xhp | 2 +- source/text/schart/01/04050100.xhp | 18 +++++++++--------- 2 files changed, 10 insertions(+), 10 deletions(-) (limited to 'source') diff --git a/source/text/scalc/guide/autofilter.xhp b/source/text/scalc/guide/autofilter.xhp index 46b2266341..8e605f9760 100644 --- a/source/text/scalc/guide/autofilter.xhp +++ b/source/text/scalc/guide/autofilter.xhp @@ -52,7 +52,7 @@ When you apply an additional AutoFilter on another column of a filtered data range, then the other combo boxes list only the filtered data. - To display all records again, select the all entry in the AutoFilter combo box. If you choose "Standard", the Standard Filter dialog appears, allowing you to set up a standard filter. Choose "Top 10" to display the highest 10 values only. + To display all records again, select the all entry in the AutoFilter combo box. If you choose Standard, the Standard Filter dialog appears, allowing you to set up a standard filter. Choose "Top 10" to display the highest 10 values only. To stop using AutoFilter, reselect all cells selected in step 1 and once again choose Data - Filter - AutoFilter. To assign different AutoFilters to different sheets, you must first define a database range on each sheet. The arithmetic functions also take account of the cells that are not visible due to an applied filter. For example, a sum of an entire column will also total the values in the filtered cells. Apply the SUBTOTAL function if only the cells visible after the application of a filter are to be taken into account. diff --git a/source/text/schart/01/04050100.xhp b/source/text/schart/01/04050100.xhp index 1b5302880d..8b863cd933 100644 --- a/source/text/schart/01/04050100.xhp +++ b/source/text/schart/01/04050100.xhp @@ -102,7 +102,7 @@ To change format of values (use less significant digits or scientific notation), select the equation in the chart, right-click to open the context menu, and choose Format Trend Line Equation - Numbers. Default equation uses x for abscissa variable, and f(x) for ordinate variable. To change these names, select the trend line, choose Format - Format Selection – Type and enter names in X Variable Name and Y Variable Name edit boxes. -To show the coefficient of determination R2, select the equation in the chart, right-click to open the context menu, and choose Insert R2. +To show the coefficient of determination R2, select the equation in the chart, right-click to open the context menu, and choose Insert R2. If intercept is forced, coefficient of determination R2 is not calculated in the same way as with free intercept. R2 values can not be compared with forced or free intercept. Trend Lines Curve Types @@ -113,16 +113,16 @@ Linear trend line: regression through equation y=a∙x+b. Intercept b can be forced. - Polynomial trend line: regression through equation y=Σ(ai∙xi). Intercept a0 can be forced. Degree of polynomial must be given (at least 2). + Polynomial trend line: regression through equation y=Σi(ai∙xi). Intercept a0 can be forced. Degree of polynomial must be given (at least 2). Logarithmic trend line: regression through equation y=a∙ln(x)+b. - Exponential trend line: regression through equation y=b∙exp(a∙x).This equation is equivalent to y=b∙mx with m=exp(a). Intercept b can be forced. + Exponential trend line: regression through equation y=b∙exp(a∙x).This equation is equivalent to y=b∙mx with m=exp(a). Intercept b can be forced. - Power trend line: regression through equation y=b∙xa. + Power trend line: regression through equation y=b∙xa. Moving average trend line: simple moving average is calculated with the n previous y-values, n being the period. No equation is available for this trend line. @@ -139,7 +139,7 @@ Exponential trend line: only positive y-values are considered, except if all y-values are negative: regression will then follow equation y=-b∙exp(a∙x). - Power trend line: only positive x-values are considered; only positive y-values are considered, except if all y-values are negative: regression will then follow equation y=-b∙xa. + Power trend line: only positive x-values are considered; only positive y-values are considered, except if all y-values are negative: regression will then follow equation y=-b∙xa. You should transform your data accordingly; it is best to work on a copy of the original data and transform the copied data. @@ -162,7 +162,7 @@ The exponential regression equation For exponential trend lines a transformation to a linear model takes place. The optimal curve fitting is related to the linear model and the results are interpreted accordingly. -The exponential regression follows the equation y=b*exp(a*x) or y=b*mx, which is transformed to ln(y)=ln(b)+a*x or ln(y)=ln(b)+ln(m)*x respectively. +The exponential regression follows the equation y=b*exp(a*x) or y=b*mx, which is transformed to ln(y)=ln(b)+a*x or ln(y)=ln(b)+ln(m)*x respectively. a = SLOPE(LN(Data_Y);Data_X) The variables for the second variation are calculated as follows: m = EXP(SLOPE(LN(Data_Y);Data_X)) @@ -172,7 +172,7 @@ Besides m, b and r2 the array function LOGEST provides additional statistics for a regression analysis. The power regression equation - For power regression curves a transformation to a linear model takes place. The power regression follows the equation y=b*x^a , which is transformed to ln(y)=ln(b)+a*ln(x). + For power regression curves a transformation to a linear model takes place. The power regression follows the equation y=b*xa, which is transformed to ln(y)=ln(b)+a*ln(x). a = SLOPE(LN(Data_Y);LN(Data_X)) b = EXP(INTERCEPT(LN(Data_Y);LN(Data_X)) r2 = RSQ(LN(Data_Y);LN(Data_X)) @@ -181,7 +181,7 @@ For polynomial regression curves a transformation to a linear model takes place. Create a table with the columns x, x2, x3, … , xn, y up to the desired degree n. Use the formula =LINEST(Data_Y,Data_X) with the complete range x to xn (without headings) as Data_X. -The first row of the LINEST output contains the coefficients of the regression polynomial, with the coefficient of xⁿ at the leftmost position. +The first row of the LINEST output contains the coefficients of the regression polynomial, with the coefficient of xn at the leftmost position. The first element of the third row of the LINEST output is the value of r2. See the LINEST function for details on proper use and an explanation of the other output parameters.
@@ -194,4 +194,4 @@
- \ No newline at end of file + -- cgit