Final answer:
False. Multicollinearity occurs when two or more independent variables are highly correlated with each other, not with the dependent variable.
Step-by-step explanation:
False. Multicollinearity occurs when two or more independent variables are highly correlated with each other, not with the dependent variable. It is a condition where independent variables in a multiple regression model are correlated to the extent that they provide redundant or overlapping information in predicting the dependent variable. This can lead to unstable and unreliable estimates of the regression coefficients.
For example, consider a multiple regression model trying to predict a person's salary using both years of experience and education level as independent variables. If years of experience and education level are strongly correlated, then multicollinearity may be present. This can make it difficult to determine the specific impact of each independent variable on the dependent variable and can lead to misleading or incorrect interpretations of the regression results.
In summary, multicollinearity occurs when there is a high correlation between independent variables, not between an independent variable and the dependent variable. Therefore, the correct answer is b. False.