Version:

Last Modified: January 9, 2017

Calculates the confidence 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 confidence interval.

Lower bound of the confidence interval.

Confidence radius of the coefficients of the fitted model.

In the following illustration, the region between the upper and lower confidence bounds is the confidence interval.

**Where This Node Can Run: **

Desktop OS: Windows

FPGA: Not supported