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.