Why analysts choose Statistico
Instead of isolated statistical outputs, Statistico keeps every stage of K-Means Clustering connected — without leaving the analytical workflow.
From partitioning to statistical reasoning — this is Interactive Statistical Computing, not a sequence of static outputs.
What the module provides
One partition. Connected cluster views.
Click a stage to inspect it — each view stays inside the same K-means solution, so there's no re-running the analysis to move between them.
k-means-overview.webpOverview
Show selected distance, k, and standardisation beside the segmentation verdict.
k-means-sizes.webpSizes
Compare how many cases land in each cluster after the current run.
k-means-centers.webpCenters
Compare cluster centers to understand what defines each segment.
k-means-profiles.webpProfiles
Read mean z-score profiles across variables for each cluster.
k-means-diagnostics.webpDiagnostics
Inspect WCSS, iterations, and assignment diagnostics for solution quality.
k-means-map.webpSeparation & Map
Explore how separated the clusters appear in reduced space.
Centroids and profiles stay linked to the same k
Changing k updates sizes, centers, and profiles together, so segment meaning is inspected across views rather than inferred from a single table.
k-means-profiles.webpAssess compactness, iterations, and separation
WCSS, iteration history, and the separation map provide complementary evidence before segments are used downstream.
Move from partition to interpretable segment profiles
Mean z-score profiles translate centroid geometry into variable-level segment stories without leaving the clustering module.
Interpretation within the analytical workflow
Statistico's AI-assisted assessment summarizes segment differences, diagnostics, and next checks from the same clustering output already in the workspace.
Built for analysts who already know the method
Excel remains the working data environment.
Statistical outputs remain visible and inspectable.
Interaction supports analysis rather than hiding it.