In a multiple regression model, the error variable $ { \varepsilon }$
is assumed to have a mean of:
A) -1.0.
B) 0.0.
C) 1.0.
D) any value smaller than -1.0.

Question

In a multiple regression model, the error variable $ { \varepsilon }$ 
 is assumed to have a mean of:
A) -1.0.
B) 0.0.
C) 1.0.
D) any value smaller than -1.0.

Quizplus · Accepted Answer

The error variable, also known as the residual, is the difference between the predicted and actual values of the dependent variable. In a multiple regression model, the goal is to minimize the sum of squared residuals. Since the residuals can be positive or negative, they will cancel each other out when aggregated. Therefore, the mean of the residuals is expected to be close to zero if the model is well-specified and the errors are normally distributed.

In a Multiple Regression Model, the Error Variable ε { \varepsilon }ε

In a Multiple Regression Model, the Error Variable ${ \varepsilon }$