<\/span><\/h3>\n1. Does an R-squared of 1 mean a perfect model?<\/b>
\nNo, even with an R-squared of 1, the model may still have limitations, such as omitted variable bias.<\/p>\n
2. Is a high R-squared always desirable?<\/b>
\nWhile a high R-squared indicates a good fit, a model with an excessively high R-squared value may suggest overfitting, which can lead to poor predictions on new data.<\/p>\n
3. Can a low R-squared value invalidate the entire model?<\/b>
\nA low R-squared alone does not invalidate the model. It might mean that the independent variables do not have a strong linear relationship with the dependent variable or that important variables have been excluded.<\/p>\n
4. Does a higher R-squared always imply better predictions?<\/b>
\nA higher R-squared does not guarantee better predictions, as it measures explanatory power rather than predictive accuracy.<\/p>\n
5. Can R-squared be negative?<\/b>
\nNo, R-squared cannot be negative. The values range from 0 to 1, inclusive.<\/p>\n