A new method called Noncentral Information Criterion (NIC) has been developed to improve model selection in linear regression. By combining the strengths of Bayesian Information Criterion (BIC) and Akaike Information Criterion (AIC), NIC outperforms both in identifying the best model for different data scenarios. This approach enhances the accuracy of model selection, making it more reliable for analyzing data with varying levels of complexity.