LibreOffice 7.1 Help
Trend lines can be added to all 2D chart types except for Pie and Stock charts.
If you insert a trend line to a chart type that uses categories, like Line or Column, then the numbers 1, 2, 3, … are used as x-values to calculate the trend line. For such charts the XY chart type might be more suitable.
To insert a trend line for a data series, select the data series in the chart. Choose
, or right-click to open the context menu, and choose .Mean Value Lines are special trend lines that show the mean value. Use
to insert mean value lines for data series.To delete a trend line or mean value line, click the line, then press the Del key.
A trend line is shown in the legend automatically. Its name can be defined in options of the trend line.
The trend line has the same color as the corresponding data series. To change the line properties, select the trend line and choose
.When the chart is in edit mode, LibreOffice gives you the equation of the trend line and the coefficient of determination R^{2}, even if they are not shown: click on the trend line to see the information in the status bar.
To show the trend line equation, select the trend line in the chart, right-click to open the context menu, and choose .
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
.Default equation uses x for abscissa variable, and f(x) for ordinate variable. To change these names, select the trend line, choose and enter names in X Variable Name and Y Variable Name edit boxes.
To show the coefficient of determination R^{2}, select the equation in the chart, right-click to open the context menu, and choose
^{2}.If intercept is forced, coefficient of determination R^{2} is not calculated in the same way as with free intercept. R^{2} values can not be compared with forced or free intercept.
The following regression types are available:
Linear trend line: regression through equation y=a∙x+b. Intercept b can be forced.
Polynomial trend line: regression through equation y=Σ_{i}(a_{i}∙x^{i}). Intercept a_{0} 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∙m^{x} with m=exp(a). Intercept b can be forced.
Power trend line: regression through equation y=b∙x^{a}.
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.
The calculation of the trend line considers only data pairs with the following values:
Logarithmic trend line: only positive x-values are considered.
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∙x^{a}.
You should transform your data accordingly; it is best to work on a copy of the original data and transform the copied data.
You can also calculate the parameters using Calc functions as follows.
The linear regression follows the equation y=m*x+b.
m = SLOPE(Data_Y;Data_X)
b = INTERCEPT(Data_Y ;Data_X)
Calculate the coefficient of determination by
r^{2} = RSQ(Data_Y;Data_X)
Besides m, b and r^{2} the array function LINEST provides additional statistics for a regression analysis.
The logarithmic regression follows the equation y=a*ln(x)+b.
a = SLOPE(Data_Y;LN(Data_X))
b = INTERCEPT(Data_Y ;LN(Data_X))
r^{2} = RSQ(Data_Y;LN(Data_X))
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*m^{x}, which is transformed to ln(y)=ln(b)+a*x or ln(y)=ln(b)+ln(m)*x respectively.
a = SLOPE(LN(Data_Y);LN(Data_X))
The variables for the second variation are calculated as follows:
m = EXP(SLOPE(LN(Data_Y);Data_X))
b = EXP(INTERCEPT(LN(Data_Y);Data_X))
Calculate the coefficient of determination by
r^{2} = RSQ(LN(Data_Y);Data_X)
Besides m, b and r^{2} the array function LOGEST provides additional statistics for a regression analysis.
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).
a = SLOPE(LN(Data_Y);LN(Data_X))
b = EXP(INTERCEPT(LN(Data_Y);LN(Data_X))
r^{2} = RSQ(LN(Data_Y);LN(Data_X))
For polynomial regression curves a transformation to a linear model takes place.
Create a table with the columns x, x^{2}, x^{3}, … , x^{n}, y up to the desired degree n.
Use the formula =LINEST(Data_Y,Data_X) with the complete range x to x^{n} (without headings) as Data_X.
The first row of the LINEST output contains the coefficients of the regression polynomial, with the coefficient of x^{n} at the leftmost position.
The first element of the third row of the LINEST output is the value of r^{2}. See the LINEST function for details on proper use and an explanation of the other output parameters.