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Polynomial curves fitting points generated with a sine function. The black dotted line is the "true" data, the red line is a first degree polynomial , the green line is second degree , the orange line is third degree and the blue line is fourth degree.
This MATLAB function returns the coefficients for a polynomial p(x) of degree n that is a best fit (in a least-squares sense) for the data in y.
Learn how to fit a polynomial function to a set of data points using least squares, maximum likelihood, or Bayesian methods. Compare different orders of polynomials, regularization techniques, and model selection criteria.
Learn how to use polyfit function to find the coefficients of a polynomial that fits a set of data in a least-squares sense. See how to plot the fit line and the equation over a finer domain.
Learn how to fit curves to your data using linear and nonlinear regression models. Compare different methods such as polynomial terms, reciprocal terms, and log transformations with examples and residual plots.
Learn how to fit polynomials and exponential equations to census data using Curve Fitting Toolbox. Compare different models based on graphical and numerical criteria, such as residuals, prediction intervals, and goodness of fit statistics.
Drag data points and their error bars and watch the best-fit polynomial curve update instantly. You choose the type of fit: linear, quadratic, or cubic. The reduced chi-square statistic shows you when the fit is good. Or you can try to find the best fit by manually adjusting fit parameters.
