Correlation Matrix Visualizer
Upload a small CSV and explore correlations between your numeric columns with a correlation matrix and heatmap. Choose Pearson or Spearman correlation, see strongest positive and negative relationships, and learn how to interpret correlation matrices.
Upload a small CSV to explore correlations
Upload a simple CSV dataset, select a few numeric columns, and we'll compute a correlation matrix with a heatmap and table so you can quickly see which variables move together. This is an educational visualizer, not a modeling engine.
Understanding Correlation Matrices
What is a Correlation Matrix?
A correlation matrix is a table showing the correlation coefficients between multiple variables. Each cell represents the strength and direction of the relationship between two variables. The diagonal always contains 1s (each variable is perfectly correlated with itself), and the matrix is symmetric around this diagonal. Correlation matrices are fundamental tools in exploratory data analysis, helping identify which variables might be related and worth investigating further.
Pearson vs Spearman Correlation
Pearson Correlation
- •Measures linear relationships
- •Assumes variables are roughly normally distributed
- •Sensitive to outliers
- •Best for continuous data with linear relationships
Spearman Correlation
- •Measures monotonic relationships
- •Uses ranks instead of raw values
- •More robust to outliers
- •Works well with ordinal data and non-linear monotonic relationships
How to Interpret a Correlation Heatmap
A correlation heatmap uses color to represent the strength and direction of correlations:
Darker/more intense colors indicate stronger correlations (closer to -1 or +1), while lighter colors indicate weaker relationships (closer to 0).
Common Pitfalls When Reading Correlations
- 1Correlation ≠ Causation: Just because two variables are correlated doesn't mean one causes the other. There could be confounding variables, reverse causation, or pure coincidence.
- 2Outliers Can Distort: A few extreme values can dramatically inflate or deflate correlation coefficients, especially Pearson correlation.
- 3Non-Linear Relationships: Pearson correlation only captures linear relationships. Two variables could have a strong non-linear relationship (like U-shaped) but show a correlation near zero.
- 4Sample Size Matters: With small samples, you can get high correlations by chance. Always consider the sample size and be skeptical of strong correlations from few observations.
- 5Range Restriction: If your data only covers a narrow range of values, correlations will appear weaker than they would be in the full population.
Limitations of This Correlation Matrix Demo
- •This is an educational tool for exploring relationships in your data, not a production-grade statistical package.
- •It does not perform hypothesis testing, calculate p-values, or provide confidence intervals for correlations.
- •It does not adjust for multiple comparisons when examining many variable pairs.
- •It cannot prove causation or control for confounding variables.
- •It is not designed for trading, investment, medical diagnosis, or any regulated advice.
Frequently Asked Questions
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