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Regression analysis

Course Description: Simple Linear Regression - Multiple Linear Regression - Predictions and Residual Analysis - Data Analysis - Indicator Variables - Testing Independent Variables - Applications of Computer Packages in Regression Analysis SAS, SPSS, Minitab, S-PLUS.
Credit hours: 3
Prerequisites: 323 STAT
Objectives of the course :

This course aims to study some mathematical and statistical methods for curve fitting and the use of statistical data to achieve this, and the extent of its application by computer.

Course outputs :

Gaining knowledge by learning some fundamental concepts related to forecasting methods and their applications. - Enhancing the ability to use and apply statistical forecasting methods in solving problems (economic, social, scientific, etc.). - Acquiring cognitive skills through thinking based on the concept of forecasting and solving some life problems. - Raising the ability to pursue advanced studies when needed in the fields of mathematics, statistics, and operations research. - Acquiring numerical skills through the application and use of forecasting methods and appropriate statistical methods.

Additional information:

Applied Linear Statistical Models (Part I). Authors: Neter et al. Translation: Dr. Anis Kanjou – Dr. Abdul Hamid Al-Zaïd – Dr. Al-Husseini Abd El-Bar
Applied Linear Regression Models, John Neter, William Wasserman, Michael Kutner, (Third Edition).

  • Last updated: 2026-06-03

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