Financial Risk Manager Part 1
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Under multiple linear regression, a residual is defined as:
Other
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NNikitesh
A
A type 1 error
B
The error sum of squares
C
The regression sum of squared deviations from the mean value of the dependent variable
D
Y - Ŷ
Explanation:
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.
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