Regression modeling is one of the most widely used statistical processes to estimate the relationships between dependent and independent variables, which have been frequently applied in a wide range of applications successfully. This method includes many techniques for modeling and analyzing several variables to cover real-world problems. The performance basis in conventional regression modeling is based on the assumption that maximum accuracy in inaccessible data is obtained from models with the least amount of error in modeling available data. In this type of regression modeling, in order to maximize the generalization ability of simulations, which are the main factor influencing the quality of decisions made in real-world problems, the principle of maximization of the accuracy of available historical data is used. However, in this type of modeling process, the model's reliability and results have not been considered. On the other, the generalization capability of a model is simultaneously dependent on the accuracy of the model and the reliability level of the accuracy. In this paper, a new methodology is proposed for multiple linear regression (MLR) modeling in which in contrast to traditionally developed models, the models' reliability is maximized instead of its accuracy. To comprehensively evaluate the proposed model's performance, 30 benchmark data sets are considered from the UCI. Empirical results indicate that, from a general perspective, in 19 cases, i.e., 63.333% of cases, the proposed model has better generalization ability than traditional ones. It is clearly illustrated the importance of the reliability of results and their accuracy that is considered in none of the conventional MLR modeling procedures. Therefore, the proposed MLR model can be regarded as an appropriate alternative in modeling fields, especially when more generalization is desired.

回归建模是最广泛使用的统计过程之一，用于估计因变量和自变量之间的关系，已成功地频繁应用于广泛的应用中。该方法包括许多用于建模和分析多个变量以涵盖现实世界问题的技术。传统回归建模的性能基础基于这样一个假设，即不可访问数据的最大精度是从建模可用数据中误差最小的模型中获得的。在这种类型的回归建模中，为了最大限度地提高模拟的泛化能力，这是影响现实世界问题决策质量的主要因素，采用了最大化可用历史数据准确性的原则。然而，在这种建模过程中，没有考虑模型的可靠性和结果。另一方面，模型的泛化能力同时依赖于模型的精度和精度的可靠性水平。在本文中，提出了一种用于多元线性回归 (MLR) 建模的新方法，与传统开发的模型相比，该方法最大限度地提高了模型的可靠性而不是其准确性。为了全面评估所提出模型的性能，考虑了来自 UCI 的 30 个基准数据集。实证结果表明，在19个案例中，即63.333%的案例中，所提出的模型比传统模型具有更好的泛化能力。它清楚地说明了结果的可靠性及其准确性的重要性，这在传统的 MLR 建模程序中都没有考虑到。因此，所提出的 MLR 模型可以被视为建模领域的合适替代方案，尤其是在需要更多泛化时。