Fit Statistics

The Generalized Linear Model computes several assessment measures to help you evaluate how well your model fits the data. These assessment measures are available at the top of the model pane. Click the currently displayed assessment measure to see all available assessment measures. The available assessment measures are the following:
-2 Log Likelihood
The likelihood function estimates the probability of an observed sample given all possible parameter values. The log likelihood is simply the logarithm of the likelihood function. This value is -2 times the log likelihood. Smaller values are preferred.
AIC
Akaike’s Information Criterion. Smaller values indicate better models. AIC values should be compared only when two models have an approximately equal number of observations. AIC values can become negative. AIC is based on the Kullback-Leibler information measure of discrepancy between the true distribution of the response variable and the distribution specified by the model.
AICC
Corrected Akaike’s Information Criterion. This version of AIC adjusts the value to account for a relatively small sample size. The result is that extra effects penalize AICC more than AIC. As the sample size increases, AICC and AIC converge.
BIC
The Bayesian Information Criterion (BIC), also known as Schwarz’s Bayesian Criterion (SBC), is an increasing function of the model’s residual sum of squares and the number of effects. Unexplained variations in the response variable and the number of effects increase the value of the BIC. As a result, a lower BIC implies either fewer explanatory variables, better fit, or both. BIC penalizes free parameters more strongly than AIC.
Last updated: January 8, 2019