Version: 1.0.1 Author: Manas Sharma Maintainer: Manas Sharma Category: Data Analysis and Statistics Entity: Manas Sharma. To fit a given set of data-points to a line. Toolbox: curvefit Title: Curve Fitters Summary: A toolbox for fitting data-points to a line, polynomial or an exponential curve using the Least Square Approximation and plot the original and fitted values.
#CURVE FITTING SCILAB CODE#
Then the following code returns the value of the slope, ‘m’ and intercept ‘c’ of the best fit-line, and also the fitted points which are basically the points of the y-axis obtained from the equation of the best fit-line. DATA FITTING IN SCILAB In this tutorial the reader can learn about data fitting, interpolation and approximation in Scilab. Let’s say you have the x-axis points stored in a matrix, ‘x’ & the y-axis points stored in a matrix ‘y’. 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' function is constructed that. Then the following code returns the value of the slope, ‘m’ and. The procedure is based on least square approximation, which, in simple words,works by finding a line that is at a minimum distance possible from all the points. Curve fitting is the process of constructing a curve, or mathematical function, that has the best fit to a series of data points, possibly subject to constraints. The procedure is based on least square approximation, which, in simple words,works by finding a line that is at a minimum distance possible from all the points. I have written a code that will calculate the equation of the best fit-line for a given set of data-points.
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