## Gaussian

Y = a + b(2πs^{2})^{-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(2πs^{2})^{-½} e^{-(x-m)2/2s2} + c(2πs^{2})^{-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