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Running principal component analysis (pca) analysis...
Sent to — interactive charts, statistical results, R code, and AI insights.
Analyze another fileStandard-library analysis: principal component analysis on the numeric columns you choose. Shows how many independent dimensions your data really has, which features move together, and how observations spread across the dominant components — with a scree chart, component summary, loadings, feature contributions, and an observation map with optional group coloring. Works on any dataset: map 2 or more numeric features.
Interactive bar visualization
Interactive table visualization
Interactive horizontal_bar visualization
Interactive scatter visualization
Interactive table visualization
Plain-English interpretation — what the numbers mean, what's significant, and what to do next.
Find how many real dimensions a wide dataset has before modelling
See our FAQ for details on pricing, data privacy, and how the analysis works. Every report includes a Methodology section showing the statistical test, assumptions checked, and diagnostics run.
Run any analysis on your own data — validated R analyses, interactive reports, AI insights, and PDF export.
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