Interactive Output (linear-AS models)
After running a Linear-AS model, the following output is available.
Model Information
- The name of the target specified on the Fields tab
- The regression weight field
- The model building method specified on the Model Selection settings
- The number of predictors input
- The number of predictors in the final model
- Akaike Information Criterion Corrected (AICC). AICC is a measure for selecting and comparing mixed models based on the -2 (Restricted) log likelihood. Smaller values indicate better models. The AICC "corrects" the AIC for small sample sizes. As the sample size increases, the AICC converges to the AIC.
- R Square. This is the goodness-of-fit measure of a linear model, sometimes called the coefficient of determination. It is the proportion of variation in the dependent variable explained by the regression model. It ranges in value from 0 to 1. Small values indicate that the model does not fit the data well.
- Adjusted R Square
Records Summary
The Records Summary view provides information about the number and percentage of records (cases) included and excluded from the model.
Predictor Importance
Typically, you will want to focus your modeling efforts on the predictor fields that matter most and consider dropping or ignoring those that matter least. The predictor importance chart helps you do this by indicating the relative importance of each predictor in estimating the model. Since the values are relative, the sum of the values for all predictors on the display is 1.0. Predictor importance does not relate to model accuracy. It just relates to the importance of each predictor in making a prediction, not whether or not the prediction is accurate.
Predicted by Observed
This displays a binned scatterplot of the predicted values on the vertical axis by the observed values on the horizontal axis. Ideally, the points should lie on a 45-degree line; this view can tell you whether any records are predicted particularly badly by the model.