In the multiple regression model $Y _ { i } = \beta _ { 0 } + \beta _ { 1 } X _ { 1 i } + \beta _ { 2 } X _ { 2 i } + \ldots + \beta _ { k } X _ { k i } + u _ { i } , i = 1 , \ldots , n$
the OLS estimators are obtained by minimizing the sum of
A) squared mistakes in $\sum _ { i = 1 } ^ { n } \left( Y _ { i } - b _ { 0 } - b _ { 1 } X _ { 1 i } - \ldots - b _ { k } X _ { k i } \right) ^ { 2 }$
B) squared mistakes in $\sum _ { i = 1 } ^ { n } \left( Y _ { i } - b _ { 0 } - b _ { 1 } X _ { 1 i } - \ldots - b _ { k } X _ { k i } - u _ { i } \right) ^ { 2 }$
C) absolute mistakes in $\sum _ { i = 1 } ^ { n } \left| \left( Y _ { i } - b _ { 0 } - b _ { 1 } X _ { 1 i } - \ldots - b _ { k } X _ { k i } \right) \right|$
D) squared mistakes in $\sum _ { i = 1 } ^ { n } \left( Y _ { i } - b _ { 0 } - b _ { 1 } X _ { i } \right) ^ { 2 }$

Question

In the multiple regression model $Y _ { i } = \beta _ { 0 } + \beta _ { 1 } X _ { 1 i } + \beta _ { 2 } X _ { 2 i } + \ldots + \beta _ { k } X _ { k i } + u _ { i } , i = 1 , \ldots , n$
the OLS estimators are obtained by minimizing the sum of
A) squared mistakes in $\sum _ { i = 1 } ^ { n } \left( Y _ { i } - b _ { 0 } - b _ { 1 } X _ { 1 i } - \ldots - b _ { k } X _ { k i } \right) ^ { 2 }$ 
B) squared mistakes in $\sum _ { i = 1 } ^ { n } \left( Y _ { i } - b _ { 0 } - b _ { 1 } X _ { 1 i } - \ldots - b _ { k } X _ { k i } - u _ { i } \right) ^ { 2 }$
C) absolute mistakes in $\sum _ { i = 1 } ^ { n } \left| \left( Y _ { i } - b _ { 0 } - b _ { 1 } X _ { 1 i } - \ldots - b _ { k } X _ { k i } \right) \right|$
D) squared mistakes in $\sum _ { i = 1 } ^ { n } \left( Y _ { i } - b _ { 0 } - b _ { 1 } X _ { i } \right) ^ { 2 }$

Quizplus · Accepted Answer

OLS estimators minimize the sum of squared mistakes in the first term.

In the Multiple Regression Model The OLS Estimators Are Obtained by Minimizing the Sum of =