# Fit Intervals (Gaussian Peak » Prediction) (G Dataflow)

Calculates the prediction interval of the best Gaussian fit for an input data set.

##### Programming Patterns

If the noise of y is Gaussian-distributed, you must fit the observations with the Curve Fitting (Gaussian Peak) node using the least square method to obtain the amplitude, center, and standard deviation.

## confidence level

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%.

## y

Dependent values. y must contain at least four points.

## x

Independent values. x must be the same size as y.

## weight

Weights for the observations.

weight must be the same size as y. The elements in weight cannot be 0. 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 in weight to 1.

## amplitude

Amplitude of the fitted model.

## error in

Error conditions that occur before this node runs.

The node responds to this input according to standard error behavior.

Standard Error Behavior

Many nodes provide an error in input and an error out output so that the node can respond to and communicate errors that occur while code is running. The value of error in specifies whether an error occurred before the node runs. Most nodes respond to values of error in in a standard, predictable way.

error in does not contain an error error in contains an error
If no error occurred before the node runs, the node begins execution normally.

If no error occurs while the node runs, it returns no error. If an error does occur while the node runs, it returns that error information as error out.

If an error occurred before the node runs, the node does not execute. Instead, it returns the error in value as error out.

Default: No error

## center

Center of the fitted model.

## standard deviation

Standard deviation of the fitted model.

## upper bound

Upper bound of the prediction interval.

## lower bound

Lower bound of the prediction interval.

## error out

Error information.

The node produces this output according to standard error behavior.

Standard Error Behavior

Many nodes provide an error in input and an error out output so that the node can respond to and communicate errors that occur while code is running. The value of error in specifies whether an error occurred before the node runs. Most nodes respond to values of error in in a standard, predictable way.

error in does not contain an error error in contains an error
If no error occurred before the node runs, the node begins execution normally.

If no error occurs while the node runs, it returns no error. If an error does occur while the node runs, it returns that error information as error out.

If an error occurred before the node runs, the node does not execute. Instead, it returns the error in value as error out.

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

Web Server: Not supported in VIs that run in a web application