On multicollinearity and artificial neural networks
One of the many problems encountered in coming up with a multiple linear regression model with estimates of continuous parameters is the presence of severe multicollinearity in the data set. In this paper, the focus is on the mathematics of multicollinearity - what it is, what it does to the model,...
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| Main Authors: | , |
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| Format: | text |
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Animo Repository
2006
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| Online Access: | https://animorepository.dlsu.edu.ph/faculty_research/13445 |
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| Institution: | De La Salle University |
| Summary: | One of the many problems encountered in coming up with a multiple linear regression model with estimates of continuous parameters is the presence of severe multicollinearity in the data set. In this paper, the focus is on the mathematics of multicollinearity - what it is, what it does to the model, how it can be detected and combated. Aside from the classical methods, artificial neural networks are also employed as an alternative to combat multicollinearity. Softwares such as Statistical Package for the Social Sciences (SPSS) Release 7.0 and 10.0 for Windows, MATLAB version 5.3 and Stuttgart Neural Network Simulator (SNNS) version 4.1 are used to carry out the massive computations in analyzing the data of the mathematics grades of the BS Mathematics graduates of the University of the Philippines. |
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