A high school administrator wants to create a model to classify future students into one of three educational tracks. The administrator randomly selects 180 students and records an achievement test score, a motivation score, and the current track for each.
Choose Stat > Multivariate > Discriminant Analysis.
In Groups, enter Track.
In Predictors, enter Test Score and Motivation.
Under Discriminant Function, ensure that Linear is selected.
Interpret the results
The Summary of Classification table shows the proportion of observations correctly placed into their true groups by the model. The school administrator uses the results to see how accurately the model classifies the students. Overall, 93.9% of students were placed into the correct educational track. Group 2 had the lowest proportion of correct placement, with only 53 of 60 students, or 88.3%, correctly placed into that educational track.
The Summary of Misclassified Observations table indicates into which group an observation should have been placed. The school administrator uses the results to see which individual students were misclassified. For example, student 4 should have been placed into group 2, but was incorrectly placed into group 1.