To derive the least squares estimator $\mu _ { Y }$ , you find the estimator m which minimizes
A) $\sum _ { i = 1 } ^ { n } \left( Y _ { i } - m \right) ^ { 2 }$
B) $\left| \sum _ { i = 1 } ^ { n } \left( Y _ { i } - m \right) \right|$
C) $\sum _ { i = 1 } ^ { n } m Y _ { i } ^ { 2 }$
D) $\sum _ { i = 1 } ^ { n } \left( Y _ { i } - m \right)$.

Question

To derive the least squares estimator $\mu _ { Y }$ , you find the estimator  m  which minimizes
A) $\sum _ { i = 1 } ^ { n } \left( Y _ { i } - m \right) ^ { 2 }$
B) $\left| \sum _ { i = 1 } ^ { n } \left( Y _ { i } - m \right) \right|$
C) $\sum _ { i = 1 } ^ { n } m Y _ { i } ^ { 2 }$
D) $\sum _ { i = 1 } ^ { n } \left( Y _ { i } - m \right)$.

Quizplus · Accepted Answer

The least squares estimator minimizes the sum of squared differences between the observed values $Y_i$ and the estimator $m$.

To Derive the Least Squares Estimator μY\mu _ { Y }μY​

To Derive the Least Squares Estimator $\mu _ { Y }$