Transform direction (here Inverse also equals to Backward)ĭivide forward transform result by number of points N (see formulas table). If your data is purely real, select imaginary item If the sampling interval is varying or real and/or imaginary data contains empty cells in the middle, the result of discrete Fourier transform will be incorrect.Ĭolumns with real and imaginary components of data. Note that using of discrete Fourier transform implies that the samples in your original data are equally spaced in time/frequency, i.e. You can set sampling interval manually in Sampling Interval field. MagicPlot calculates sampling interval as a difference between second and first values in Sampling column. Sampling interval of original data Δ t is used to compute the data in resulting sampling column. XN - anchor point x-coordinates yN - anchor point y-coordinatesĪ - area (integral) dx - half width at halfĪs for original Gaussian: a - amplitude dx - half width at halfĪs for original Lorentzian: a - amplitude dx - half width at half It means that changing the first parameter compensates changing of the second one so that the fitting algorithm cannot select between them. If two parameters are linked the corresponding matrix value will be close to 1.
The values lie in range -1…1, diagonal elements are always 1. This matrix shows if the parameters are linked. Here α is the matrix of partial derivatives of fit function with respect to parameters β m and β n which is also used by fitting algorithm to compute parameters for next iteration. This statistic is always smaller than R 2, can decrease as you add new fit curves or introduce parameters, and even be negative for poorly fitting models.
This is a biased estimate of the population R 2, and will never decrease if additional fit parameters (fit curves) are added, even if they are irrelevant.Īdjusted R 2 (or degrees of freedom adjusted R-square) is a slightly modified version of R 2, designed to penalize for the excess number of fit parameters which do not add to the explanatory power of the regression. R 2 will be equal to one if fit is perfect, and to zero otherwise.
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