Interpreting R-squared Values
Suppose you build a linear regression model predicting house prices based on square footage. However, R² alone doesn’t indicate whether the model is accurate or unbiased—you must check other diagnostics, such as residual plots, p-values, and adjusted R-squared. This example underscores the importance of relying on adjusted R-squared when assessing complex models, as it corrects for the inflation of R² due to additional predictors. While R-squared (and its adjusted variant) is a useful metric in regression analysis, it is not without limitations. When introducing additional predictors into a r squared interpretation regression model,…