What is a characteristic of a good predictor in regression analysis?

A characteristic of a good predictor in regression analysis is a high R² value. This value represents the proportion of variance in the dependent variable that can be explained by the independent variable(s). When the R² value is high, it indicates that the model explains a significant portion of the variability in the outcome, which suggests that the predictor has a strong relationship with the dependent variable. In practical terms, a high R² value contributes to a more reliable and effective regression model, helping to ensure that the insights gathered from the analysis can be trusted and are informative.

A high R² value is often sought after in model evaluation as it provides a quantitative measure of the model’s explanatory power. However, while a high R² is favorable, it's also important to consider other factors such as the p-value of individual predictors to assess their significance, and to be cautious of overfitting.

The other options, while they may pertain to different aspects of regression analysis, do not embody the definitive characteristic of a good predictor. A low p-value indicates statistical significance but doesn’t alone guarantee that the predictor is effective or strong. High multi-collinearity suggests that two or more predictors are highly correlated, which can distort the results of regression analysis. A negative correlation can