A proposed Method for the Analysis of Multiple Regression using Artificial Intelligence | ||
| المجلة العلمية لقطاع کليات التجارة بجامعة الأزهر | ||
| Article 16, Volume 19, Issue 1, 2018, Pages 1-39 PDF (1.92 M) | ||
| Document Type: المقالة الأصلية | ||
| DOI: 10.21608/jsfc.2018.85801 | ||
| Author | ||
| Mohamed Abdel Salam Agamy | ||
| Mohamed Abdel Salam agami Assistant Professor of Statistics Mahmoud El-Sayed Wahba Demonstrator at Department of Statistics Faculty of Commerce - Al-Azhar University | ||
| Abstract | ||
| This research presents a proposed method to overcome difficulties which we face when we use traditional regression analysis method and provide solutions in an automated manner that does not require experience or a large field study. Some of these difficulties are mathematical difficulties, some of which are the need for considerable experience to choose the appropriate data model, especially when the traditional regression analysis method is not available. The new approach raises forecast accuracy with powerful Neural Network MATLAB code. This approach automatically trains neural networks and applies them for regression analysis, thus getting accurate, business or stock market predictions doesn’t require much effort or time. This means faster and more precise results than ever before. | ||
Highlights | ||
This research aims to: 1. Introducing a new method for the analysis of multiple regression using artificial neural networks, which represent one of the most important areas of artificial intelligence. 2. Comparing the suggested method of artificial neural networks with the traditional method of multiple regression analysis to determine which is better for prediction. 3- Conducting an applied study using realistic data. 4 - Formulation of a computer program using MATLAB software packages to compare the two methods. 5. Formulate a program using MATLAB software packages to predict regression models using both neural networks and conventional methods | ||
| Keywords | ||
| The Analysis of; Multiple Regression; Artificial; Intelligence | ||
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