Error sum of squares (ESS) is computed as
A)$$\sum _ { i = 1 } ^ { n } \left( \hat { Y } _ { i } - Y _ { i } \right)$$
B)$$\sum _ { i = 1 } ^ { n } \left( \hat { Y } _ { i } - Y _ { i } \right) ^ { 2 }$$
C)$$\sum _ { i = 1 } ^ { n } \left( \hat { Y } _ { i } - X _ { i } \right)$$
D)$$\sum _ { i = 1 } ^ { n } \left( Y _ { i } - \hat { Y } _ { i } \right) ^ { 2 }$$

Question

Quizplus · Accepted Answer

The Error Sum of Squares (ESS) is calculated as the sum of the squared differences between the observed values $Y_i$ and the predicted values $\hat{Y}_i$.

Error Sum of Squares (ESS) Is Computed as A) ∑i=1n(Y^i−Yi)\sum _ { i = 1 } ^ { n } \left( \hat { Y } _ { i } - Y _ { i } \right)i=1∑n​(Y^i​−Yi​)

Error Sum of Squares (ESS) Is Computed as
A) $\sum _ { i = 1 } ^ { n } \left( \hat { Y } _ { i } - Y _ { i } \right)$