Are the residuals clustered or heteroscedastic ?Дoes the curve follow the trend of the data? Or does the curve systematically deviate from the trend of the data? Prism offers two tests that answer these questions. These tests make no sense and are not available if you chose robust or Poisson regression. We recommend the D'Agostino-Pearson test. Prism can test this assumption by running a normality test on the residuals. Least-squares nonlinear regression assumes that the distribution of residuals follows a Gaussian distribution (robust nonlinear regression does not make this assumption). The other two values will be of interest only to those who have studied Poisson regression in depth. The pseudo-R2 can be interpreted pretty much like an ordinary R2.

If you chose Poisson regression, Prism offers three ways to quantify the goodness of fit: the pseudo-R2, the dispersion index and the model deviance. It turns out that this value underestimates the SD a bit, so the RSDR is computed by multiplying that empirical value by n/(n-K), where K is the number of parameters fit. We therefore calculate the range that contains 68.27% of the residuals. In a Gaussian distribution, 68.27% of values lie within one standard deviation of the mean. The goal here is to compute a robust standard deviation, without being influenced by outliers. If you chose robust regression, Prism can compute a Robust Standard Deviation of the Residuals (RSDR). If the data or weighting are not identical between fits, then any comparison of AICc values would be meaningless. But note that it only makes sense to compare AICc between fits when the only difference is the model you chose. You can then use the AICc to choose between them. The AICc is useful only if you separately fit the same data to three or more models.We recommend the Sy.x, which is also called Se. Sy.x and RMSE are alternative ways to quantify the standard deviation of the residuals.Reporting this value is useful only if you want to compare Prism's results to those of another program, or you want to do additional calculations by hand. The sum-of-squares (or weighted sum-of-squares) is the value that Prism minimizes when it fits a curve.It is not commonly reported with nonlinear regression. The adjusted R2 takes into account the number of parameters fit to the data, so has a lower value than R 2 (unless you fit only one parameter, in which case R 2 and adjusted R 2 are identical).R 2 is the standard way to assess goodness-of-fit.The choices vary depending on what fitting method you chose in the Methods tab.

Graphpad prism 8 curve fitting guide