In this episode, we step into multivariate thinking and ask a practical question: when do data points naturally form “groups,” and how can we use those groups to make decisions?
We walk through how grouping methods decide what’s “close” or “similar,” then compare two main approaches—building clusters step by step versus forming clusters all at once. You’ll also hear how tree-like visual summaries help us see structure in messy data, and how the same multivariate ideas can be flipped into classification, where the goal is to assign a new case to the most likely group.
