Conducting Cluster Analysis The DUNGAREE data set gives the number of pairs of four different types of dungarees sold at stores over a specific time period. Each row represents an individual store. There are six columns in the data set. One column is the store identification number, and the remaining columns contain the number of pairs of each type of jeans sold. a. Create a new diagram in your project. Name the diagram Jeans. b. Define the data set DUNGAREE as a data source. c. Determine whether the model roles and measurement levels assigned to the variables are appropriate. Examine the distribution of the variables. - Are there any unusual data values? - Are there missing values that should be replaced? Assign the variable STOREID the model role ID and the variable SALESTOT the model role Rejected. Make sure that the remaining variables have the Input model role and the Interval measurement level. Why should the variable SALESTOT be rejected? Add an Input Data Source node to the diagram workspace and select the DUNGAREE data table as the data source. - Add a Cluster node to the diagram workspace and connect it to the Input Data node. Select the Cluster node. Leave the default setting as Internal Standardization Standardization. What would happen if inputs were not standardized? Run the diagram from the Cluster node and examine the results. Does the number of clusters created seem reasonable? Specify a maximum of six clusters and rerun the Cluster node. How does the number and quality of clusters compare to that obtained in part h ? Use the Segment Profile node to summarize the nature of the clusters. Discuss