What does the statistical measure “R-squared” indicate in regression analysis?

The statistical measure "R-squared," also known as the coefficient of determination, quantifies the proportion of the variance in the dependent variable that can be attributed to the independent variables in a regression model. In simpler terms, it reflects how well the model fits the data—it tells us how much of the variability in the outcome variable can be explained by the predictors.

A higher R-squared value indicates that a greater proportion of the variance is accounted for by the independent variables, suggesting a better fit of the model to the actual data. Conversely, a lower R-squared value would imply that the model does not explain much of the variability in the dependent variable. This statistical measure is crucial for understanding the effectiveness of the regression model in capturing the relationships between variables, thus helping in making informed decisions based on the analysis.

In the context of property tax assessments and mass appraisals, R-squared can help assess how well the selected features (such as location, size, and condition of the property) accurately predict property values.