A comparison between general linear regression and quantitative robust regression (data for kidney patients as an example)
Regression analysis is considered one of the statistical methods that are widely used several papers because it describes the relationship between variables in the form of an equation and is defined as a statistical tool used to find out the relationship between the dependent variable and one or mo...
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| Main Authors: | , |
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| Format: | Article |
| Language: | Arabic |
| Published: |
University of Kufa, Faculty of Administration and Economics
2024-09-01
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| Series: | مجلة الغري للعلوم الاقتصادية والادارية |
| Subjects: | |
| Online Access: | https://journal.uokufa.edu.iq/index.php/ghjec/article/view/17265 |
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| Summary: | Regression analysis is considered one of the statistical methods that are widely used several papers because it describes the relationship between variables in the form of an equation and is defined as a statistical tool used to find out the relationship between the dependent variable and one or more independent variables. The research problem was the presence of outliers in the data of the dependent variable and this leads to the inefficiency of the linear regression model estimated using general linear regression due to the failure to meet the conditions for using Ordinary least squares method (OLS), and thus a lack of confidence in its estimated and predictive accuracy, The aim of the research is to use quantitative robust regression on a sample divided into three quartiles (0.25, 0.50, 0.75) represented by (74) male kidney patients in Al-Kadhimiya Hospital for the period (1/2024-3/2024) and compare it with general linear regression and choose the best model using Comparison standards (Hannan & Quinn, Schwartz Bayesian, Akaike Information), to determine the extent of the effect of independent variables (Diabetes, Hypertension, triglycerides, age) on the increase in blood urea, and the researcher reached the advantage of quantitative robust regression (first quartile and second quartile) compared to general linear regression, While general linear regression appeared better than quantitative robust regression (third quartile), All independent variables have a significant effect on the increase in blood urea levels in all three models.
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| ISSN: | 3006-1911 3006-192X |