What does R-squared represent in regression interpretation?

R-squared, also known as the coefficient of determination, quantifies the proportion of variance in the dependent variable that can be explained by the independent variables in a regression model. It provides insight into how well the model accounts for the variability of the data points in relation to the overall average.

A value of R-squared ranges from 0 to 1, with 0 indicating that the independent variables explain none of the variability of the dependent variable, and 1 indicating that they explain all the variability. Therefore, when interpreting R-squared, a higher value signifies that a greater proportion of variance is accounted for by the model, illustrating its explanatory power.

This concept is critical in evaluating the efficacy of a regression analysis, as it directly relates to the model's ability to predict outcomes based on input data. Understanding R-squared aids analysts and researchers in recognizing the model's strengths and limitations, and guides their decisions about model selection and refinement.

In comparison, the other options reference different aspects of regression analysis. While the overall fit of the model is related to R-squared, it does not specifically define what R-squared measures. The average value of predictions and significance levels pertain to other assessments within regression analysis, such as prediction accuracy and