In the multiple regression model, the SER is given by
A) $\frac { 1 } { n - 2 } \sum _ { i = 1 } ^ { n } \hat { u } _ { i }$
B) $\frac { 1 } { n - k - 2 } \sum _ { i = 1 } ^ { n } u _ { i }$
C) $\frac { 1 } { n - k - 2 } \sum _ { i = 1 } ^ { n } \hat { u } _ { i }$
D) $\frac { 1 } { n - k - 2 } \sum _ { i = 1 } ^ { n }\hat{ u} _ { i } ^ { 2 }$

Question

Quizplus · Accepted Answer

The Standard Error of the Regression (SER) is calculated using the sum of squared residuals divided by the degrees of freedom, which is $n - k - 2$ for a multiple regression model with $k$ predictors.

In the Multiple Regression Model, the SER Is Given by 1n−2∑i=1nu^i\frac { 1 } { n - 2 } \sum _ { i = 1 } ^ { n } \hat { u } _ { i }n−21​∑i=1n​u^i​

In the Multiple Regression Model, the SER Is Given by $\frac { 1 } { n - 2 } \sum _ { i = 1 } ^ { n } \hat { u } _ { i }$