When comparing two regression models, which model should be preferred based on the R-squared metric?

In the context of regression analysis, the R-squared metric is crucial as it indicates how well the independent variables explain the variability of the dependent variable. A higher R-squared value suggests that a model is able to explain a greater proportion of variance in the outcome variable.

Choosing the model with the higher R-squared, in this case, Model Y indicates that it has more explanatory power compared to the other model. This means that the predictions made by Model Y are likely to be more accurate because it accounts for a larger percentage of the variability in the outcome data. Such a model typically has better fit, which can be crucial for practical applications where understanding the relationship and variation in the data is important.

While other options may address aspects like generalizability or flexibility, they do not capture the fundamental interpretation of R-squared. The essence of model selection based on this metric is straightforward: the model that explains more variance should be prioritized for its capacity to provide better insights and predictions.