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首页> 外文期刊>International Journal of Statistics and Applications >On the Efficiency of Multiple Linear Regression over Artificial Neural Network Models
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On the Efficiency of Multiple Linear Regression over Artificial Neural Network Models

机译：论人工神经网络模型多线性回归的效率

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摘要

There has been a considerable and continuous interest to develop models for rapid and accurate modeling of students’ academic performances. In this study, an Artificial Neural Network model (ANNm) and a Multiple Linear Regression model (MLRm) were used to model the academic performance of university students. The accuracy of the models was judged by model evaluation criteria like and The modeling ability of the developed ANN model architecture was compared with a MLR model using the same training data sets. The squared regression coefficients of prediction for MLR and ANN models were 0.746 and 0.893, respectively. The results revealed that ANN model proved more accurate in modeling the data set, as compared with MLR model. This was because ANN model had its as against the traditional model which it’s was 0.182. Based on the results of this study, ANN model could be used as a promising approach for rapid modeling and prediction in the academic fields.
机译：开发用于快速准确建模的学生学术表演的模型有相当多的兴趣。在本研究中，使用人工神经网络模型（Annm）和多个线性回归模型（MLRM）来模拟大学生的学术表现。通过模型评估标准判断模型的准确性，以及使用相同的训练数据集的MLR模型将开发的ANN模型架构的建模能力进行了比较。对MLR和ANN模型预测的平方回归系数分别为0.746和0.893。结果表明，与MLR模型相比，ANN模型在建模数据集中被证明更准确。这是因为ANN模型与传统模式有0.182。基于本研究的结果，ANN模型可作为学术领域快速建模和预测的有希望的方法。

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