If a group of independent variables are not significant individually but are significant as a group at a specified level of significance,this is most likely due to
A) collinearity.
B) the absence of dummy variables.
C) the presence of dummy variables.
D) autocorrelation.

Question

Quizplus · Accepted Answer

Collinearity refers to a situation where two or more predictor variables in a multiple regression model are highly correlated, meaning that one can be linearly predicted from the others with a substantial degree of accuracy. When variables are collinear, it can be difficult to determine the individual impact of each variable on the dependent variable because they tend to change in unison. However, when these variables are considered together as a group, their collective impact on the dependent variable can be significant, even if the individual contributions are not clear due to the shared variance among them. This is why a group of independent variables might not be significant individually but can be significant as a group, indicating collinearity among the variables.

If a Group of Independent Variables Are Not Significant Individually