The residual, eᵢ, is the difference between the observed value, Yᵢ, and the predicted value from the regression, Ŷᵢ.
Eᵢ = Yᵢ - Ŷᵢ = Yᵢ - (b₀ + b₁X₁ᵢ + b₂X₂ᵢ + ... + bₖXₖᵢ)
Why other options are incorrect:
- A: Type I error is the rejection of a true null hypothesis, not related to regression residuals.
- B: The error sum of squares (RSS) is the sum of squared residuals, not the residual itself.
- C: The regression sum of squares (SSR) measures the variation explained by the regression model, not individual residuals.
