The standard error of the regression (SER)is defined as follows A) $\frac { 1 } { n - 2 } \sum _ { i = 1 } ^ { n } \hat { u } _ { l } ^ { 2 }$ B)SSR C)1-R² D) $\frac { 1 } { n - 1 } \sum _ { i = 1 } ^ { n } \hat { u } _ { i } ^ { 2 }$

Question

Quizplus · Accepted Answer

The standard error of the regression (SER) is correctly defined by option A, which represents the square root of the average squared deviations of the observed values from the values predicted by the model, adjusted for degrees of freedom (n - 2). Option D is incorrect because it divides by $n - 1$, which is typically used for the sample variance calculation, not for SER. Options B and C are incorrect as they do not represent the formula for SER; SSR refers to the sum of squared residuals, and option C is nonsensical.

The Standard Error of the Regression (SER)is Defined as Follows 1n−2∑i=1nu^l2\frac { 1 } { n - 2 } \sum _ { i = 1 } ^ { n } \hat { u } _ { l } ^ { 2 }n−21​∑i=1n​u^l2​

The Standard Error of the Regression (SER)is Defined as Follows $\frac { 1 } { n - 2 } \sum _ { i = 1 } ^ { n } \hat { u } _ { l } ^ { 2 }$