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Running principal component analysis (pca) analysis...
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Analyze another fileReduces many numeric variables to a few principal components that capture the most variance. Shows how much information each component explains and which original variables drive it.
Use this when you have many numeric variables and want to simplify your data or visualize high-dimensional patterns.
If you want clusters (not components), use K-Means or DBSCAN.
Built for: Data scientist, researcher, bioinformatician
Typical data source: Wide numeric dataset with many columns to reduce
High-dimensional numeric data
Minimum 30 rows · Best with 100-10000 rows
Reduce high-dimensional numeric data to its most informative components using Principal Component Analysis. Identifies which variables drive variance, reveals hidden structure, and produces scree plots, score plots, and variable loading heatmaps.
Variance explained per principal component
Running total of variance captured by components
Observations plotted on first two principal components
Correlation between original variables and principal components
Eigenvalues and variance explained per component
Strongest variable contributions per component
Plain-English interpretation — what the numbers mean, what's significant, and what to do next.
Need something simpler? Correlation — Just want correlations, not dimensionality reduction
Need more power? Dbscan — Want to cluster in reduced space
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 — 60+ validated R modules, interactive reports, AI insights, and PDF export.
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