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Running exploratory data analysis - high-dimensional demo analysis...
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Analyze another fileComprehensive exploratory analysis for high-dimensional datasets with mixed variable types.
Use this when you need exploratory analysis on your data.
See related tools for alternatives.
Built for: Analyst, data scientist, business user
Typical data source: CSV with relevant columns
Data for exploratory analysis
Minimum 10 rows · Best with 100-5000 rows
Comprehensive exploratory analysis template for high-dimensional datasets with mixed numeric and categorical variables. Demonstrates full EDA capabilities with bivariate comparisons, statistical tests, normality checks, pairwise plots, cross-tabulation, and feature importance ranking.
Summary statistics for all numeric variables
Distribution of each numeric variable
Pairwise Pearson correlations between numeric variables
Missing data patterns across variables
Pairwise relationships between key variables
ANOVA and Kruskal-Wallis tests for group differences
Shapiro-Wilk normality test for each numeric variable
Distribution of the dependent variable
Relative importance of variables for predicting the target
Scatterplot matrix of top numeric variables
Data quality and integrity checks
Plain-English interpretation — what the numbers mean, what's significant, and what to do next.
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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