# Curve Fitting and Interpolation

The technique of curve fitting analysis extracts a set of curve parameters or coefficients from a data set to obtain a functional description of the data set.

The LabWindows/CVI Advanced Analysis Library includes the following curve fitting and interpolation functions:

Function | Description
--- | ---
CubicSplineFit | Uses cubic spline fitting to fit the data set (x,y).
ExpFitEx | Fits the data set (x, y) to the exponential model using the Least Square, Least Absolute Residual, or Bisquare method.
ExpFitInterval | Calculates the confidence interval for the best exponential fitting function or the prediction interval for the observations.
GaussFit | Fits the data set (x, y) to the Gaussian model using the Least Square, Least Absolute Residual, or Bisquare method.
GaussFitInterval | Calculates the confidence interval for the best Gaussian fitting function or the prediction interval for the observations.
GenLSFit | Finds the best fit k-dimensional plane and the set of linear coefficients using the least chi-squares method for observation data sets.
GenLSFitCoef | Finds the set of linear fit coefficients, which describe the linear curve that best represents the input data that GenLSFitCoef uses to obtain the least squares solution technique.
GoodnessOfFit | Calculates three parameters, the summation of square error, a normalized parameter to measure the goodness of fit, and the root mean square error, which describe how well a fitted model matches the original data set.
LinearFitEx | Fits the data set (x, y) to the linear model using the Least Square, Least Absolute Residual, or Bisquare method.
LinearFitInterval | Calculates the confidence interval for the best linear fitting function or the prediction interval for the observations.
LogFit | Fits the data set (x, y) to the logarithm model using the Least Square, Least Absolute Residual, or Bisquare method.
LogFitInterval | Calculates the confidence interval for the best logarithm fitting function or the prediction interval for the observations.
NonLinearFit | Uses the Levenberg-Marquardt algorithm to determine the least squares set of coefficients that best fit the set of input data points (X, Y) as expressed by a nonlinear function y = f(x, a) where a is the set of coefficients.
NonLinearFitWithMaxIters | Uses the Levenberg-Marquardt algorithm to determine the least squares set of coefficients that best fit the set of input data points (X,Y) as expressed by a nonlinear function y = f(x, a) where a is the set of coefficients. If NonLinearFitWithMaxIters reaches the maximum number of iterations without reaching a solution, it returns an error.
PolyFitEx | Fits the data set (x, y) to a polynomial model using the Least Square method.
PolyInterp | Calculates the value of the unique polynomial and returns an estimate of the error in the interpolation.
PowerFit | Fits the data set (x, y) to the power model using the Least Square, Least Absolute Residual, or Bisquare method.
PowerFitInterval | Calculates the confidence interval for the best power fitting function or the prediction interval for the observations.
RatInterp | Returns the value of a particular rational function and an estimate of the error in the interpolation.
RemoveOutlierByIndex | Removes the outliers specified by the given indices.
RemoveOutlierByRange | Removes the outliers according to the input range.
SpInterp | Performs a cubic spline interpolation of a function.
Spline | Calculates the second derivatives used by the cubic spline interpolant.