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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| Format: | Article |
| Language: | Arabic |
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University of Kufa, Faculty of Administration and Economics
2024-09-01
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| Series: | مجلة الغري للعلوم الاقتصادية والادارية |
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| Online Access: | https://journal.uokufa.edu.iq/index.php/ghjec/article/view/17265 |
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| _version_ | 1846157957028380672 |
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| author | أسامة عبد العزيز كاظم القريشي سما سعدي علي الهاشمي |
| author_facet | أسامة عبد العزيز كاظم القريشي سما سعدي علي الهاشمي |
| author_sort | أسامة عبد العزيز كاظم القريشي |
| collection | DOAJ |
| description | 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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| format | Article |
| id | doaj-art-79d5ab05daa34c04af1601cd6e96d3da |
| institution | Kabale University |
| issn | 3006-1911 3006-192X |
| language | Arabic |
| publishDate | 2024-09-01 |
| publisher | University of Kufa, Faculty of Administration and Economics |
| record_format | Article |
| series | مجلة الغري للعلوم الاقتصادية والادارية |
| spelling | doaj-art-79d5ab05daa34c04af1601cd6e96d3da2024-11-24T20:04:55ZaraUniversity of Kufa, Faculty of Administration and Economicsمجلة الغري للعلوم الاقتصادية والادارية3006-19113006-192X2024-09-0120310.36325/ghjec.v20i3.17265A comparison between general linear regression and quantitative robust regression (data for kidney patients as an example)أسامة عبد العزيز كاظم القريشي0سما سعدي علي الهاشمي1Ministry of Education IraqRusafa Management Institute, BaghdadRegression 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. https://journal.uokufa.edu.iq/index.php/ghjec/article/view/17265General linear regression , Quantile regression, Robust |
| spellingShingle | أسامة عبد العزيز كاظم القريشي سما سعدي علي الهاشمي A comparison between general linear regression and quantitative robust regression (data for kidney patients as an example) مجلة الغري للعلوم الاقتصادية والادارية General linear regression , Quantile regression, Robust |
| title | A comparison between general linear regression and quantitative robust regression (data for kidney patients as an example) |
| title_full | A comparison between general linear regression and quantitative robust regression (data for kidney patients as an example) |
| title_fullStr | A comparison between general linear regression and quantitative robust regression (data for kidney patients as an example) |
| title_full_unstemmed | A comparison between general linear regression and quantitative robust regression (data for kidney patients as an example) |
| title_short | A comparison between general linear regression and quantitative robust regression (data for kidney patients as an example) |
| title_sort | comparison between general linear regression and quantitative robust regression data for kidney patients as an example |
| topic | General linear regression , Quantile regression, Robust |
| url | https://journal.uokufa.edu.iq/index.php/ghjec/article/view/17265 |
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