📈 Interactive Regression & Model Selection
Fit multiple regression models to your data, compare them with AIC/BIC, and visualise the best fit.
📊 Paste your data
Two columns: X (independent) and Y (dependent). Separate with spaces, tabs, or commas.
⚙️ Model Selection
Hold Ctrl/Cmd to select multiple.
💡 Instructions: Enter your data, select models, and click "Fit Models". The tool will fit each model and show comparison metrics.
📖 Model Selection Criteria
AIC (Akaike Information Criterion): Estimates prediction error. Lower is better. Penalizes model complexity.
BIC (Bayesian Information Criterion): Similar to AIC but penalizes complexity more strongly. Lower is better.
R²: Proportion of variance explained. Higher is better (but can be misleading with polynomial models).
Adjusted R²: Penalizes adding predictors. Useful for comparing models with different numbers of parameters.