Consider two full rank linear models y X 1 1 l and y X 2 , where all predictors in the first model ( 1 ) are also contained in the second model () Show that the SS Res for the first model is at least the SS Res for the second model

Question: Consider two full rank linear models y = X 1 1 + l and y = X + 2 , where

Consider two full rank linear models y = X ₁γ ₁ + ε _l and y = X _β + ε ₂, where all predictors in the first model (γ ₁) are also contained in the second model (β). Show that the SS _Resfor the first model is at least the SS _Res for the second model.

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To demonstrate that the residual sum of squares SS Res for the first model is at least the SS Res for the second model we can use the properties of li... View full answer

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