What does R-squared measure in regression analysis?

R-squared is a statistical measure that indicates the proportion of the variance in the dependent variable that can be explained by the independent variable(s) in a regression model. It provides insight into the explanatory power of the model, showcasing how well the chosen variables account for the variability of the outcome being studied.

When R-squared is close to 1, it indicates that a large proportion of the variance in the dependent variable is explained by the independent variable(s), suggesting a strong relationship. Conversely, a low R-squared value implies that the model does not explain much of the variability in the response variable, indicating a weaker relationship.

In the context of regression analysis, understanding the explanatory power is crucial as it helps researchers and analysts assess the effectiveness of the model in making predictions or understanding relationships between variables. Thus, R-squared serves as an essential metric in evaluating model performance and guiding further analysis or model refinement.