Step 1. Bring up Cluster analysis dialog box.

Step 2. Configure all cluster setup.

Step 3. Interpret agglomeration schedule and dendrogram.

Look into Cluster 1 and Cluster 2 as observation index number.

Step 4. Identify cluster to retain.

3 Cluster to be retain.

Step 5. CLU3_1 will be the clustering column.

Step 6. Identify each cluster contain how many observations.

Step 7. Name each cluster.

Cluster 1 Independent variables mostly lowest, except hdl & ldl. Lowest lipid profile.

Cluster 2 Independent variables mostly moderate. Moderate lipid profile.

Cluster 3 Independent variables mostly highest, except hdl & ldl. Cluster 3 has high lipid profile except hdl & ldl.

Step 8. Test mean value are same across all 3 cluster.

H₀ : Mean of the clustering variables are the same across clusters.- If same, there are no variation between clusters.

H₁ : At least one of the mean of the clustering variable differs from the other across clusters.

All of the independent variable Sig Value < 0.05; reject H₀.

Step 1. Bring up Cluster analysis dialog box.

Step 2. Configure all cluster setup.

Step 3. Name each cluster.

Cluster 2 is high lipid profile except hdl.

Cluster 3 is moderate lipid profile.

Cluster 1 is low lipid profile except hdl.

Step 4. Test mean value are same across all 3 cluster.

H₀ : Mean of the cluster variables are equal across clusters.

H₁ : At least one of the mean differs from across clusters.

All variable is significant except hdl. The mean of hdl is equal across cluster. Hdl is not a good clustering variable.

Most influential variable must look into “F” statistic. The largest belong to subsep having F statistic value of 246.798. Subsep is the most influential variable in deciding cluster. Hdl is the lowest influential variable in deciding the cluster, and share consistent p-value > 0.05.