What does multicollinearity refer to in regression analysis?

• A. Where independent variables are uncorrelated
• B. Where one independent variable is highly correlated with the dependent variable
• C. Where two or more independent variables are highly correlated with each other
• D. Where there is a perfect prediction of the dependent variable

Answer: (C)

Multicollinearity refers to a situation in regression analysis where two or more independent variables are highly correlated with each other. This correlation can create complications in estimating the coefficients of the regression model. When independent variables are multicollinear, it becomes difficult to determine the individual effect of each variable on the dependent variable due to the overlapping information they provide. Consequently, this can lead to inflated standard errors, making it harder to assess the significance of each predictor in the model.

In the context of regression analysis, it's crucial to recognize multicollinearity because it may affect the reliability of the statistical inferences drawn from the model. Understanding this concept helps analysts make informed decisions regarding variable selection and model specification to ensure clear insights from the data.