Calculates the prediction interval of the best polynomial fit for an input data set.
If the noise of y is Gaussian-distributed, you must fit the observations with the Polynomial mode of the Curve Fitting node using the Least Square method to obtain polynomial coefficients.
Level of certainty for the confidence interval. confidence level must be greater than 0 and less than 1.
Default: 0.95, which means the probability that the best fit falls between lower bound and upper bound is 95%.
Dependent values. The number of sample points in y greater than the number of elements in polynomial coefficients.
Independent values. x must be the same size as y.
Weights for the observations.
weight must be the same size as y. weight also must contain non-zero elements. If an element in weight is less than 0, this node uses the absolute value of the element. If you do not wire an input to weight, this node sets all elements of weight to 1.
Coefficients of the fitted model in ascending order of power. If the total number of elements in polynomial coefficients is m, the polynomial order is m - 1.
Error conditions that occur before this node runs. The node responds to this input according to standard error behavior.
Default: No error
Upper bound of the prediction interval.
Lower bound of the prediction interval.
Error information. The node produces this output according to standard error behavior.
In the following illustration, the region between the upper and lower prediction bounds is the prediction interval.
Where This Node Can Run:
Desktop OS: Windows
FPGA: Not supported