Which concept describes the situation where predictor variables are highly correlated, making it difficult to distinguish their effects?

Which concept describes the situation where predictor variables are highly correlated, making it difficult to distinguish their effects?

• A. Multicollinearity
• B. Heteroskedasticity
• C. Outliers
• D. Cook's Distance

Answer: (A)

Multicollinearity occurs when two or more predictors move together, so the model can’t separate their individual effects. This makes coefficient estimates unstable and increases their standard errors, which in turn makes it hard to tell which predictor is truly driving the outcome. You may also see inflated VIF values, signaling redundancy among the variables. To address it, you can drop one of the correlated predictors, combine them into a single variable, or use regularization methods that shrink coefficients. Heteroskedasticity refers to non-constant variance of the errors, not relationships among predictors. Outliers are extreme observations that can distort results, and Cook’s Distance measures how influential a single observation is to the overall fit.

Multicollinearity occurs when two or more predictors move together, so the model can’t separate their individual effects. This makes coefficient estimates unstable and increases their standard errors, which in turn makes it hard to tell which predictor is truly driving the outcome. You may also see inflated VIF values, signaling redundancy among the variables. To address it, you can drop one of the correlated predictors, combine them into a single variable, or use regularization methods that shrink coefficients.

Heteroskedasticity refers to non-constant variance of the errors, not relationships among predictors. Outliers are extreme observations that can distort results, and Cook’s Distance measures how influential a single observation is to the overall fit.