# Fit Intervals (Logarithm » Prediction) (G Dataflow)

Version:

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

##### Programming Patterns

If the noise of y is Gaussian-distributed, you must fit the observations with the Logarithm mode of the Curve Fitting node using the Least Square method to obtain the amplitude and scale.

## base

Base of the logarithm.

Name Value Description
e 2.71828 Uses the natural logarithm.
10 10 Uses 10 as the base of the logarithm.
2 2 Uses 2 as the base of the logarithm.

Default: e

## 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 three 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. 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.

## 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.

Default: No error

## scale

Scale 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.

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