Inclusion of redundant independent variables in the regression model increases the problem of __________.
A)autocorrelation
B)independence
C)Multicollinearity
D)outliers

Question

Quizplus · Accepted Answer

Including redundant independent variables in the regression model leads to multicollinearity. Multicollinearity is a problem because it inflates the standard errors of the regression coefficients, making it hard to identify the true contribution of each variable in the model. Moreover, multicollinearity makes it hard to distinguish the individual effects of independent variables, hence making the model's predictions unreliable.

Inclusion of Redundant Independent Variables in the Regression Model Increases