After using the segmentation and classification tool (without discrimination) in MEXL, the data result has shown in the graph below. The results contain all 160 entries into 9 clusters, and they are grouped into like segments. In order to identify the number of distinct segments better, I chose to select the option of statistical difference to highlight the outlier difference in each cluster. Through selecting 9 different clusters out of 160 observation results, the distance between each cluster is somewhat well spaced with minimal outliers. The main outlier in the group being cluster five being the one segment that has a greater distance in comparison to the other segments. As the segments become larger through merging, the distance between them become further showing variation as more variables are being considered. In the end the total distance is found at the top once all data is looked at as a whole entity rather than individual results.
To help identify segments/clusters, we used the Hierarchical methods to build up the data and created a Dendogram, which is the following.