Adjusted deviances are measures of variation for different components of the model. The order of the predictors in the model does not affect the calculation of the adjusted deviances. Minitab separates the deviance into different components that describe the deviance from different sources.
- The adjusted deviance for the regression model quantifies the difference between the current model and the full model.
- The adjusted deviance for a term quantifies the difference between a model with that term and the full model.
- The adjusted deviance for error quantifies the deviance that the model does not explain.
- The total adjusted deviance is the sum of the adjusted deviance for the model and the adjusted deviance for error. The total adjusted deviance quantifies the total deviance in the data.
Minitab uses the adjusted deviances to calculate the p-value for a term. Minitab also uses the adjusted deviances to calculate the deviance R2 statistic. Usually, you interpret the p-values and the R2 statistic instead of the deviances.