Answer: Y - Ŷ

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.

Author: Nikitesh Somanthe