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Curve fitting [1] [2] is the process of constructing a curve, or mathematical function, that has the best fit to a series of data points, [3] possibly subject to constraints. [ 4 ] [ 5 ] Curve fitting can involve either interpolation , [ 6 ] [ 7 ] where an exact fit to the data is required, or smoothing , [ 8 ] [ 9 ] in which a "smooth ...
T. Strutz: Data Fitting and Uncertainty (A practical introduction to weighted least squares and beyond). 2nd edition, Springer Vieweg, 2016, ISBN 978-3-658-11455-8. H. P. Gavin, The Levenberg-Marquardt method for nonlinear least-squares curve-fitting problems (MATLAB implementation included)
To fit a symmetrical distribution to data obeying a negatively skewed distribution (i.e. skewed to the left, with mean < mode, and with a right hand tail this is shorter than the left hand tail) one could use the squared values of the data to accomplish the fit. More generally one can raise the data to a power p in order to fit symmetrical ...
One common difficulty in fitting nonlinear models is finding adequate starting values. A major advantage of rational function models is the ability to compute starting values using a linear least squares fit. To do this, p points are chosen from the data set, with p denoting the number of parameters in the rational model. For example, given the ...
Linear interpolation on a set of data points (x 0, y 0), (x 1, y 1), ..., (x n, y n) is defined as piecewise linear, resulting from the concatenation of linear segment interpolants between each pair of data points. This results in a continuous curve, with a discontinuous derivative (in general), thus of differentiability class.
Fitting a linear model to a given data set usually requires ... fits a curve model to the data, ... 100 random fitted values using Matlab ...
Simplifying a piecewise linear curve with the Douglas–Peucker algorithm. The starting curve is an ordered set of points or lines and the distance dimension ε > 0. The algorithm recursively divides the line. Initially it is given all the points between the first and last point. It automatically marks the first and last point to be kept.
In this example, the Gauss–Newton algorithm will be used to fit a model to some data by minimizing the sum of squares of errors between the data and model's predictions. In a biology experiment studying the relation between substrate concentration [S] and reaction rate in an enzyme-mediated reaction, the data in the following table were obtained.