Calculates three statistical parameters that describe how well a fitted model matches the original data set.
Array of dependent values of the original data set. The number of elements in y must be greater than degree of freedom.
Array of dependent values of the fitted model. best fit must be the same size as y.
Array of weights for the observations.
weight must be the same size as y. If you do not wire an input to weight, this node sets all elements of weight to 1. If an element in weight is less than 0, this node uses the absolute value of the element.
Length of the array of dependent values of the original data set minus the number of coefficients in the fitted model. If degree of freedom is less than or equal to 0, this node sets degree of freedom to the length of y minus 2.
Error conditions that occur before this node runs. The node responds to this input according to standard error behavior.
Default: No error
Summation of square error. The smaller the SSE, the better the fit.
A normalized parameter to measure the goodness of fit. The closer to 1 the R-square, the better the fit.
Root mean square error. The smaller the RMSE, the better the fit.
Error information. The node produces this output according to standard error behavior.
The statistical parameters SSE, R-square, and RMSE are defined by the following equations:
Where This Node Can Run:
Desktop OS: Windows
FPGA: Not supported