The term "r²" (pronounced "r squared") refers to the coefficient of determination in statistics. It measures how well a statistical model explains and predicts future outcomes. Specifically, r² indicates the proportion of variance in the dependent variable that can be explained by the independent variable(s) in a regression analysis. Values range from 0 to 1, where 0 means no explanatory power and 1 means perfect explanation. In practical terms, an r² value of 0.8 suggests that 80% of the variability in the outcome can be explained by the model, while the remaining 20% is due to other factors. This makes r² a useful tool for assessing the effectiveness of models in fields like economics, psychology, and machine learning.