The classification table shows the practical results of using the discriminant model. Of the cases used to create the model, 94 of the 124 people who previously defaulted are classified correctly. 281 of the 375 nondefaulters are classified correctly. Overall, 75.2% of the cases are classified correctly. Classifications based upon the cases used to create the model tend to be too "optimistic" in the sense that their classification rate is inflated. The cross-validated section of the table attempts to correct this by classifying each case while leaving it out from the model calculations; however, this method is generally still more "optimistic" than subset validation. Subset validation is obtained by classifying past customers who were not used to create the model. These results are shown in the Cases Not Selected section of the table. 77.1 percent of these cases were correctly classified by the model. This suggests that, overall, your model is in fact correct about three out of four times. The 150 ungrouped cases are the prospective customers, and the results here simply give a frequency table of the model-predicted groupings of these customers.