R 2 = RSQ(LN(Data_Y) LN(Data_X)) The polynomial regression equationįor polynomial regression curves a transformation to a linear model takes place.Ĭreate a table with the columns x, x 2, x 3, …, x n, y up to the desired degree n. The power regression follows the equation y=b*x a, which is transformed to ln(y)=ln(b)+a*ln(x). The power regression equationįor power regression curves a transformation to a linear model takes place. The variables for the second variation are calculated as follows:īesides m, b and r 2 the array function LOGEST provides additional statistics for a regression analysis. 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. The optimal curve fitting is related to the linear model and the results are interpreted accordingly. R 2 = RSQ(Data_Y LN(Data_X)) The exponential regression equationįor exponential trend lines a transformation to a linear model takes place. The logarithmic regression follows the equation y=a*ln(x)+b. The linear regression follows the equation y=m*x+b.Ĭalculate the coefficient of determination byīesides m, b and r 2 the array function LINEST provides additional statistics for a regression analysis. You can also calculate the parameters using Calc functions as follows. You should transform your data accordingly it is best to work on a copy of the original data and transform the copied data. 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.
Logarithmic trend line: only positive x-values are considered.Įxponential 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). The calculation of the trend line considers only data pairs with the following values: No equation is available for this trend line. Moving average trend line: simple moving average is calculated with the n previous y-values, n being the period. Power trend line: regression through equation y=b∙x a. Logarithmic trend line: regression through equation y=a∙ln(x)+b.Įxponential trend line: regression through equation y=b∙exp(a∙x).This equation is equivalent to y=b∙m x with m=exp(a). Degree of polynomial must be given (at least 2). Polynomial trend line: regression through equation y=Σ i (a i ∙x i ). Linear trend line: regression through equation y=a∙x+b. The following regression types are available:
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R 2 values can not be compared with forced or free intercept. If intercept is forced, coefficient of determination R 2 is not calculated in the same way as with free intercept. To show the coefficient of determination R 2, select the equation in the chart, right-click to open the context menu, and choose Insert R 2. 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 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.ĭefault equation uses x for abscissa variable, and f(x) for ordinate variable. 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. 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.
Trend Line Equation and Coefficient of Determination To change the line properties, select the trend line and choose Format - Format Selection - Line. The trend line has the same color as the corresponding data series. Its name can be defined in options of the trend line. A trend line is shown in the legend automatically.