Axuda do LibreOffice 24.8
Pódense engadir liñas de tendencia a todos os tipos de gráficas en 2D, excepto as de sector e de cotizacións
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, first double-click the chart to enter edit mode and select the data series in the chart to which a trend line is to be created.
Choose
, or right-click the data series 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.
The menu item
is only available when the chart is in edit mode. It will appear grayed out if the chart is in edit mode but no data series is selected.The trend line has the same color as the corresponding data series. To change the line properties, select the trend line and choose
.A trend line is shown in the legend automatically. Its name can be defined in options of the trend line.
When the chart is in edit mode, LibreOffice gives you the equation of the trend line and the coefficient of determination R2, 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 R2, select the equation in the chart, right-click to open the context menu, and choose
.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.
Están dispoñíbeis os tipos de regresión seguintes:
Linear trend line: regression through equation y=a∙x+b. Intercept b can be forced.
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.
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.
O cálculo da liña de tendencia considera só pares de datos cos valores seguintes:
Liña de tendencia logarítmica: só se consideran valores positivos de x.
Liña de tendencia exponencial: só se consideran valores de y positivos, excepto se todos os valores de y foren negativos: neste caso a regresión sigue a ecuación seguinte 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.
Ten que transformar os seus datos de acordo con isto; é mellor traballar cunha copia dos datos orixinais e transformar os datos copiados.
Tamén pode calcular os parámetros empregando as funcións do Calc como segue.
A regresión lineal segue a ecuación y=m*x+b.
m = SLOPE(Data_Y;Data_X)
b = INTERCEPT(Data_Y ;Data_X)
Calculate the coefficient of determination by
r2 = RSQ(Data_Y;Data_X)
Besides m, b and r2 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))
r2 = 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*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))
b = EXP(INTERCEPT(LN(Data_Y);Data_X))
Calculate the coefficient of determination by
r2 = RSQ(LN(Data_Y);Data_X)
Besides m, b and r2 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*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))
Para curvas de regresión polinomial realízase unha transformación a un modelo lineal.
Crear unha táboa coas columnas x, x2, x3, … , xn, y até o grao desexado 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 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.