Unformatted text preview: expected value of Y when evaluating: First, insert the true expected value of Y i ˆ 1 i 2 i 1 i 1 2 1 2 1 i ( + + ) x X X E [ ] = x ∑ ∑ , and : ˆ 1 i 2 i 2 i 1 i 1 i 1 i 1 1 2 1 2 2 2 2 1 i 1 i 1 i x x x X X X E [ ] = + = + x x x ∑ ∑ ∑ ∑ ∑ ∑ . which says that the expected value of 1 ˆ equals the true effect of X 1 on Y , 1 , plus the bias due to model misspecification . The bias due to model misspecification is made up of two parts: (1) 2 the true effect of X 2 on Y ; and (2) the relationship between X 2 and X 1 . Moral of this story: Leaving out an important independent variable can lead to biased parameter estimates....
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 Winter '10
 DanielLass
 Yi, Errors and residuals in statistics

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