What does high correlation among predictor variables in a regression suggest?

High correlation among predictor variables in a regression analysis suggests that they are measuring similar aspects. When predictor variables are highly correlated, it often indicates that they share a common underlying characteristic or construct. This can lead to multicollinearity, which may impact the estimates of the regression coefficients and make it difficult to determine the individual effect of each predictor. Understanding the nature of these correlations is crucial for model interpretation and ensuring that the predictors provide unique information about the outcome variable.

On the other hand, the other choices provide incorrect interpretations of the implications of high correlation. For instance, stating that they measure different constructs contradicts the essence of high correlation, which implies they are likely tapping into similar underlying factors rather than differing ones. Saying there is no relationship is inaccurate, as high correlation indicates a strong relationship between the variables.