Trend Lines /text/schart/01/04050100.xhp calculating;regression curves regression curves in charts trend lines in charts mean value lines in charts only use trend line, not regression curve: i89823 Trend Lines Trend lines can be added to all 2D chart types except for Pie and Stock charts.
Linear A linear trend line is shown.Logarithmic A logarithmic trend line is shown.Exponential An exponential trend line is shown.Power A power trend line is shown.Polynomial A polynomial trend line is shown with a given degree.Degree Degree of polynomial trend line.Moving average A moving average trend line is shown with a given period.Period Number of points to calculate average of moving average trend line.Trend line name Name of trend line in legend.Extrapolate forward Trend line is extrapolated for higher x-values.Extrapolate backward Trend line is extrapolated for lower x-values.Force intercept For linear, polynomial and exponential trend lines, intercept value is forced to a given value.Intercept value Value of intercept if it is forced.Show equation Shows the trend line equation next to the trend line.Show correlation coefficient (R2) Shows the coefficient of determination next to the trend line.X name Name of X variable in trend line equation.Y name Name of Y variable in trend line equation. 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 Insert - Trend Line, or right-click the data series to open the context menu, and choose Insert Trend Line. Mean Value Lines are special trend lines that show the mean value. Use Insert - Mean Value Lines 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 Insert - Trend Line 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 Format - Format Selection - Line. A trend line is shown in the legend automatically. Its name can be defined in options of the trend line. Trend Line Equation and Coefficient of Determination When the chart is in edit mode, %PRODUCTNAME 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 Insert Trend Line Equation. 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. 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 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=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. Constraints 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∙xa. You should transform your data accordingly; it is best to work on a copy of the original data and transform the copied data. Calculate Parameters in Calc You can also calculate the parameters using Calc functions as follows. The linear regression equation 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 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 equation 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)) 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. 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. The power regression equation 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)) The polynomial regression equation 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 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.
X/Y Error Bars LINEST function LOGEST function SLOPE function INTERCEPT function RSQ function