Version:

Last Modified: January 9, 2017

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.

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