Two simple regression models were used to predict a single dependent variable.Both models were highly significant, but when the two independent variables were placed in the same multiple regression model for the dependent variable, did not increase substantially and the parameter estimates for the model were not significantly different from 0.This is probably an example of collinearity.

Question

Two simple regression models were used to predict a single dependent variable.Both models were highly significant, but when the two independent variables were placed in the same multiple regression model for the dependent variable,  did not increase substantially and the parameter estimates for the model were not significantly different from 0.This is probably an example of collinearity.

Quizplus · Accepted Answer

The statement suggests that the two independent variables are highly correlated, leading to collinearity in the multiple regression model. This is a common issue in regression analysis, where the presence of collinearity can lead to unstable and inaccurate estimates of the model parameters. This is because collinearity occurs when there is a high correlation between the independent variables, which can lead to unstable and unreliable parameter estimates in the multiple regression model. In this case, the fact that the parameter estimates were not significantly different from 0 suggests that the independent variables may be highly correlated, which is indicative of collinearity.

Two Simple Regression Models Were Used to Predict a Single