In multiple regression models, the values of the error variable $\varepsilon$ are assumed to be:
A) autocorrelated.
B) dependent on each other.
C) independent of each other.
D) always positive.

Question

Quizplus · Accepted Answer

In multiple regression models, the error term $\varepsilon$ denotes the difference between the predicted values and actual values of the response variable. The values of this error term are assumed to be independent of each other, meaning that the value of $\varepsilon_i$ does not depend on the value of $\varepsilon_j$ for i≠j. Autocorrelation or dependent errors violate this assumption and can lead to biased and inefficient estimates of the regression coefficients. Positive error values are possible, but they can also be negative depending on the direction of the discrepancy between predicted and actual values.

In Multiple Regression Models, the Values of the Error Variable ε\varepsilonε

In Multiple Regression Models, the Values of the Error Variable $\varepsilon$