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Running correlation analysis analysis...
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Analyze another fileComputes pairwise correlations between all numeric variables and visualizes them as a heatmap. Highlights the strongest positive and negative relationships in your data.
Use this when you have multiple numeric columns and want to see which variables move together.
If you need to predict one variable from others, use Regression. If you need clusters, use DBSCAN or K-Means.
Built for: Analyst, researcher, data scientist, student
Typical data source: Any CSV with 3+ numeric columns
Numeric dataset with multiple variables
Minimum 20 rows · Best with 100-5000 rows
Compute and visualize pairwise Pearson or Spearman correlations between numeric variables. Produces a correlation matrix heatmap, ranked bar chart of top correlations, scatter plot of the strongest relationship, and variable distribution summaries.
Pairwise correlations between all numeric variables
Strongest variable relationships ranked by absolute correlation
Scatter plot of the most strongly correlated pair
Distribution shapes and outliers for each variable
Full pairwise correlation statistics with significance
Plain-English interpretation — what the numbers mean, what's significant, and what to do next.
Need something simpler? Auto Profiler — Want a full data profile, not just correlations
Need more power? Elastic Net — Want to model one variable as a function of others
Similar: Benchmarking
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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