I have already run a full cluster analysis for an undergraduate Economics assignment and now need help interpreting the results. The technical side—coding and model fitting—is done; my sticking point is explaining what the clusters actually mean and defending the number of clusters chosen. You would guide me through: • reading distance measures, dendrograms, elbow/silhouette outputs and any goodness-of-fit statistics • translating those statistical patterns into clear economic insights I can write up • recognising limitations, assumptions and robustness checks that an instructor will expect Acceptance criteria By the end of our session(s) I should be able to: • justify the selected cluster solution in plain language • describe the defining traits of each cluster in an economic context • outline at least two potential caveats and how to address them in my report We can work in whichever tool you prefer—R, Python (scikit-learn), Stata or SPSS—using screen-share and a shared document. I’m hoping to schedule at least one 60–90 minute live session in the next few days. When you reply, tell me about your experience teaching statistics and any similar projects you have helped interpret.