What does tolerance indicate in relation to multicollinearity?

Tolerance is a statistical measure that indicates the presence of multicollinearity in a regression model. Specifically, it is calculated as 1 minus the squared correlation coefficient between a predictor variable and all other predictor variables in the model (1 - r²).

When the tolerance value is low, it suggests that a predictor variable is highly correlated with other predictor variables, leading to potential multicollinearity issues. This can result in unstable coefficient estimates and other problems in the regression analysis, making it difficult to assess the effect of each predictor on the outcome variable.

Choosing this option accurately reflects that lower tolerance values indicate a problem with multicollinearity, emphasizing its importance in regression diagnostics. The other choices incorrectly define how tolerance operates or misinterpret its implications in the context of multicollinearity. For example, using 1 + r² misrepresents the formula and its interpretation entirely, while focusing on independent variables. Understanding tolerance correctly is crucial for identifying multicollinearity and ensuring sound analysis in regression modeling.