📊R-squared (Coefficient of Determination)
R² (coefficient of determination) measures the proportion of variance in the dependent variable that is predictable from the independent variable(s). R²=0.80 means 80% of variation in Y is explained by the model.
- 1R² = 1 − SS_residual / SS_total
- 2SS_total = Σ(yi − ȳ)²
- 3SS_residual = Σ(yi − ŷi)²
- 4For simple linear regression: R² = r²
SS_total=100 · SS_residual=30=R² = 0.70 — model explains 70% of varianceReasonable for many real-world applications
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Fun Fact
R² always increases when adding more predictors — even random ones. Adjusted R² corrects for this by penalising model complexity.
References
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