What do higher values of R² in linear regression signify?

Higher values of R² in linear regression indicate a stronger relationship between the independent and dependent variables. Specifically, R², or the coefficient of determination, quantifies the proportion of variance in the dependent variable that can be explained by the independent variable(s). A higher R² value suggests that the model's predictions are closely aligned with the actual data points, indicating a good fit.

This increased alignment means that the model can more reliably predict the outcome based on the input variables, leading to greater confidence in its predictive power. In practical terms, a high R² makes it more likely that changes in the independent variable(s) will result in predictable changes in the dependent variable, reinforcing the model's validity.

Other choices point toward misunderstandings of R²: lower variability or increased uncertainty would imply a greater inability to predict outcomes accurately, while a poor fit would naturally be associated with a low R² value. Similarly, the idea of no relationship suggests an R² close to zero, which is contrary to what higher values signify, making the correct understanding of R² essential in validating regression models.