Correlation Matrix Generator
Overview
The Correlation Matrix Generator calculates Pearson correlation coefficients for all pairs of numeric variables and presents them in both tabular and visual heatmap formats. Upload CSV data and instantly see which variables are strongly related, making it essential for feature selection in machine learning, multicollinearity detection in regression, and exploratory data analysis.
Tips
- Focus on correlations above 0.7 or below -0.7 as these indicate strong linear relationships worth investigating
- For machine learning, remove one variable from highly correlated pairs (>0.9) to reduce redundancy and multicollinearity
- Remember correlation does not imply causation - strong correlations require domain knowledge to interpret correctly
- Clean your data first by removing or imputing missing values, as the tool cannot handle incomplete datasets
- Use at least 30 observations for reliable correlation estimates; smaller samples can produce misleading coefficients
- Check for outliers before analyzing, as extreme values can artificially inflate or deflate correlation coefficients
- The heatmap’s color gradient (red for negative, blue for positive) provides quick visual identification of relationship patterns