Quantitative Business Analysis (QBA) Exam 2 Practice Test

A value of R-squared near 1 can signal overfitting when the data set is small or there are many predictors. R-squared measures how much of the variation in the observed outcome is explained by the model, but it is an in-sample, training metric. Adding predictors tends to raise R-squared regardless of whether those predictors truly capture real, generalizable relationships, so with limited data you can end up fitting random noise rather than a true signal. This makes the model look almost perfectly predictive on the data you used, while its performance on new data may be poor. To guard against this, look at adjusted R-squared, which penalizes extra predictors and often lowers the value if those predictors don’t add real explanatory power. Cross-validation or a separate test set provides a better check of generalization. In short, an R-squared near 1 in small samples or with many predictors is a warning sign of overfitting rather than a guarantee of real predictive strength.