## 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