Keywords
Abstract
Regression analysis is one of the fundamental methods in data analysis used for prediction and forecasting. This paper explores the application of definite integrals in regression models, particularly in improving accuracy in predicting nonlinear trends. By incorporating definite integrals, we demonstrate how smoothing techniques and error minimization can enhance predictive capabilities in economic and scientific domains. Experimental results indicate a significant improvement in model accuracy compared to traditional approaches.
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