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Experimental Analysis of Methods Used to Solve Linear Regression Models

Mua’ad Abu-Faraj1,*, Abeer Al-Hyari2, Ziad Alqadi3

1 Computer Information Systems Department, The University of Jordan, Aqaba, 77110, Jordan
2 Electrical Engineering Department, Al-Balqa Applied University, As Salt, 19117, Jordan
3 Computers and Networks Engineering Department, Amman, 15008, Jordan

* Corresponding Author: Mua’ad Abu-Faraj. Email: email

Computers, Materials & Continua 2022, 72(3), 5699-5712.


Predicting the value of one or more variables using the values of other variables is a very important process in the various engineering experiments that include large data that are difficult to obtain using different measurement processes. Regression is one of the most important types of supervised machine learning, in which labeled data is used to build a prediction model, regression can be classified into three different categories: linear, polynomial, and logistic. In this research paper, different methods will be implemented to solve the linear regression problem, where there is a linear relationship between the target and the predicted output. Various methods for linear regression will be analyzed using the calculated Mean Square Error (MSE) between the target values and the predicted outputs. A huge set of regression samples will be used to construct the training dataset with selected sizes. A detailed comparison will be performed between three methods, including least-square fit; Feed-Forward Artificial Neural Network (FFANN), and Cascade Feed-Forward Artificial Neural Network (CFFANN), and recommendations will be raised. The proposed method has been tested in this research on random data samples, and the results were compared with the results of the most common method, which is the linear multiple regression method. It should be noted here that the procedures for building and testing the neural network will remain constant even if another sample of data is used.


Cite This Article

APA Style
Abu-Faraj, M., Al-Hyari, A., Alqadi, Z. (2022). Experimental analysis of methods used to solve linear regression models. Computers, Materials & Continua, 72(3), 5699-5712.
Vancouver Style
Abu-Faraj M, Al-Hyari A, Alqadi Z. Experimental analysis of methods used to solve linear regression models. Comput Mater Contin. 2022;72(3):5699-5712
IEEE Style
M. Abu-Faraj, A. Al-Hyari, and Z. Alqadi "Experimental Analysis of Methods Used to Solve Linear Regression Models," Comput. Mater. Contin., vol. 72, no. 3, pp. 5699-5712. 2022.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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