Gaussian Curves

Gaussian

Y = a + b(2πs²)^-½ e^-(x-m)2/2s2

( Y = a + b/(sqrt(2*pi)*s) * exp(-(x-m)**2/2*s**2) )

The Gaussian curve is a bell-shaped curve like the Normal probability density.

Double Gaussian

Y = a + b(2πs²)^-½ e^-(x-m)2/2s2 + c(2πs²)^-½ e^-(x-n)2/2s2

( Y = a + b/(sqrt(2*pi)*s) * exp(-(x-m)**2/2*s**2) + c/(sqrt(2*pi)*s) * exp(-(x-n)**2/2*s**2) )

The double Gaussian is the sum of two overlapping Gaussian curves, with means m and n. This model is currently restricted to have equal standard deviation (s) for the two components.

See also

• Standard Curves for information on general options and other curves
• Options for choosing which results to display
• Further Output for additional output subsequent to analysis
• Standard Curves with Correlated Errors for fitting curves with correlated errors
• Saving Results for further analysis
• Fitted Model for graphical display of the model
• Model Checking for diagnostic plots for model checking
• Examples of Standard Nonlinear Curves
• Exponential Curves
• Fourier Curves
• Growth Curves
• Rational Curves
• Nonlinear Models menu
• Nonlinear Quantile Regression menu
• FITCURVE directive
• FITNONLINEAR directive
• FIT directive
• RQNONLINEAR procedure
• MINIMIZE procedure
• SIMPLEX procedure