Note that fit is not built-in; it's part of the curve fitting toolbox I found that the MATLAB "fit" function was slow, and used "lsqcurvefit" with an inline Gaussian function. This is for fitting a Gaussian FUNCTION, if you just want to fit data to a Normal distribution, use "normfit." Check it nPoints = 200; binSize = 1/nPoints ;

This is for fitting a Gaussian FUNCTION, if you just want to fit data to a Normal distribution, use "normfit." Check it nPoints = 200; binSize = 1/nPoints ; fauxMean = 47 ;fauxStd = 8; faux = fauxStd.*randn(1,nPoints) + fauxMean; % REPLACE WITH YOUR ACTUAL DATA xaxis = 1:length(faux) ;fauxData = histc(faux,xaxis);

The data is meant to be Gaussian already, but for some filtering reasons, they will not perfectly match the prescribed and expected Gaussian distribution. I therefore aim to reduce the existing scatter between data and desired distribution. For example, my data fit the Gaussian distribution as follows:

For example, Gaussian peaks can describe line emission spectra and chemical concentration assays. Open the Curve Fitter app by entering curveFitter at the MATLAB ® command line. Alternatively, on the Apps tab, in the Math, Statistics and Optimization group, click Curve Fitter. In the Curve Fitter app, select curve data.