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, R² 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

Quizplus · Accepted Answer

When two independent variables are highly correlated, they are said to be collinear. In such cases, the multiple regression model may not improve the prediction accuracy significantly as compared to the simple regression models. Additionally, the parameter estimates for the collinear independent variables become unstable and may not be significantly different from 0. This description of the models indicates the presence of collinearity.

Two Simple Regression Models Were Used to Predict a Single