Which of the following is true about the residual sum of squares in regression models?

The statement that the residual sum of squares (RSS) indicates the fit of a model relative to the mean is true. In regression analysis, the RSS quantifies the discrepancy between the observed data and the predicted values from the regression model. By measuring how much the predictions deviate from the actual observations, the RSS provides insight into how well the model captures the underlying relationships in the data.

More specifically, the RSS is calculated by taking the sum of the squares of the residuals, where a residual is the difference between the observed value and the predicted value. A smaller RSS indicates that the model's predictions are closely aligned with the actual data points, suggesting a better fit to the observed values relative to simply using the mean of the dependent variable.

The context for the other options highlights why they do not align with the definition and properties of RSS. While it is true that minimizing RSS is a concept in regression model selection, it is not guaranteed to be minimized across all models since the choice of model complexity can lead to varied RSS values. The assertion that RSS cannot be negative holds because it is based on squared differences, which are non-negative; thus, while true, it does not provide insight into the model fit. Lastly, while the RSS does relate to