LibreOffice 7.5 Help
Performs linear, logarithmic, or power regression analysis of a data set comprising one dependent variable and multiple independent variables.
For example, a crop yield (dependent variable) may be related to rainfall, temperature conditions, sunshine, humidity, soil quality and more, all of them independent variables.
For more information on regression analysis, refer to the corresponding Wikipedia article.
Enter a single range that contains multiple independent variable observations (along columns or rows). All X variable observations need to be entered adjacent to each other in the same table.
Enter the range that contains the dependent variable whose regression is to be calculated.
Check to use the first line (or column) of the data sets as variable names in the output range.
The reference of the top left cell of the range where the results will be displayed.
Set the regression type. Three types are available:
Linear Regression: finds a linear function in the form of y = b + a1.[x1] + a2.[x2] + a3.[x3] ..., where ai is the i-th slope, [xi] is the i-th independent variable, and b is the intercept that best fits the data.
Logarithmic regression: finds a logarithmic curve in the form of y = b + a1.ln[x1] + a2.ln[x2] + a3.ln[x3] ..., where ai is the i-th coefficient, b is the intercept and ln[xi] is the natural logarithm of the i-th independent variable, that best fits the data.
Power regression: finds a power curve in the form of y = exp( b + a1.ln[x1] + a2.ln[x2] + a3.ln[x3] ...), where ai is the i-th power, [xi] is the i-th independent variable, and b is intercept that best fits the data.
A numeric value between 0 and 1 (exclusive), default is 0.95. Calc uses this percentage to compute the corresponding confidence intervals for each of the estimates (namely the slopes and intercept).
Select whether to opt in or out of computing the residuals, which may be beneficial in cases where you are interested only in the slopes and intercept estimates and their statistics. The residuals give information on how far the actual data points deviate from the predicted data points, based on the regression model.
Calculates the regression model using zero as the intercept, thus forcing the model to pass through the origin.