Curve Fitting using MATLAB

Curve Fitting can be defined as the empirical determination of a curve or function that approximates a set of data. Curve fitting can involve either interpolation, where an exact fit to the data is required, or smoothing, in which a "smooth" function is constructed that approximately fits the data.

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  • Changing Appearance of Curve Fit Lines
  • Curve fitting by polynomial regression
  • Custom Linear and Nonlinear Regression
  • Displaying the Curve Fit Equation
  • Entering a Custom Curve Fit Definition
  • Exponential Curve Fitting
  • Fitting Equations with Multiple Independent Variables
  • Forcing a Linear Fit Through the Origin
  • Getting Curve Fit Values into the Data Window
  • Interpreting Regression¬† & Curve Fit Results
  • KaleidaGraph’s Smooth & Least Squares Curve Fits
  • Least Squares Curve Fits
  • Linear and Nonlinear Regression
  • Logarithmic & Power Function Curve Fitting
  • Minimum mean square error method
  • Nonlinear & Polynomial Curve Fitting
  • Programmatic Curve and Surface Fitting
  • The General Curve Fit and the Macro Calculator
  • The KaleidaGraph Guide to Curve Fitting