Multicollinearity is an issue happened when independent variables in the model are highly correlated.
True
False
The correct answer and explanation is:
Answer: True
Multicollinearity occurs when two or more independent variables in a regression model are highly correlated with each other. This makes it difficult to determine the individual effect of each independent variable on the dependent variable. In the presence of multicollinearity, the coefficients of the correlated independent variables become unstable and may lead to unreliable estimates.
When independent variables are highly correlated, it means that they provide similar information about the variance in the dependent variable. This can cause issues in estimating the effects of each predictor accurately. For example, if two variables are highly correlated, it may become unclear whether the changes in the dependent variable are due to one variable or the other, or both.
One of the most common signs of multicollinearity is when the p-values of the coefficients of correlated independent variables are very high, indicating that the variables are not significantly contributing to the model despite expectations. Furthermore, the standard errors of the regression coefficients will be inflated, making it harder to assess the significance of the predictors.
To detect multicollinearity, methods such as the Variance Inflation Factor (VIF) are often used. A VIF greater than 10 suggests high multicollinearity and may require remedial action, such as removing one of the correlated variables, combining them into a single variable, or using regularization techniques like Ridge regression to mitigate its effect.
In summary, multicollinearity is an important issue to consider when building regression models because it can lead to misleading results, making it challenging to interpret the individual impact of each predictor on the outcome.