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Electrical Conductivity as a General Predictor of Multiple Parameters in Tigris River Based on Statistical Regression Model
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Surface water samples from different locations within Tigris River's boundaries in Baghdad city have been analyzed for drinking purposes. Correlation coefficients among different parameters were determined. An attempt has been made to develop linear regression equations to predict the concentration of water quality constituents having significant correlation coefficients with electrical conductivity (EC). This study aims to find five regression models produced and validated using electrical conductivity as a predictor to predict total hardness (TH), calcium (Ca), chloride (Cl), sulfate (SO4), and total dissolved solids (TDS). The five models showed good/excellent prediction ability of the parameters mentioned above, which is a very good startup to establish a rule of thumb in the laboratories to compare between observations. The importance of linear regression equations in predicting surface water quality parameters is a method that can be applied to any other location.

 

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Publication Date
Mon Feb 01 2021
Journal Name
Https://www.researchgate.net/journal/university-of-baghdad-engineering-journal-1726-4073
Electrical Conductivity as a General Predictor of Multiple Parameters in Tigris River Based on Statistical Regression Model
...Show More Authors

Surface water samples from different locations within Tigris River's boundaries in Baghdad city have been analyzed for drinking purposes. Correlation coefficients among different parameters were determined. An attempt has been made to develop linear regression equations to predict the concentration of water quality constituents having significant correlation coefficients with electrical conductivity (EC). This study aims to find five regression models produced and validated using electrical conductivity as a predictor to predict total hardness (TH), calcium (Ca), chloride (Cl), sulfate (SO4), and total dissolved solids (TDS). The five models showed good/excellent prediction ability of the parameters mentioned above, which is a very

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Publication Date
Fri Jun 30 2023
Journal Name
Iraqi Journal Of Science
Salivary Immune Parameters as A Predictor of Oral Diseases
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Diabetes mellitus (DM) is a metabolic and hormonal disorder in which the body does not produce sufficient or respond ordinarily to insulin, leading to an increase in blood sugar (glucose) levels. This disordered has many side effects on body health, one of them being oral health. This study aimed to find these effects on several oral immune parameters included (IL6, CRP, and alpha-amylase) and the possible use of these parameters in the prediction of oral health and further risk sequel. A total of 91 specimens including 51 DM patients and 40 apparently healthy individuals were enrolled in this study which was carried out from November/2021 to February/2022. The results revealed that, abnormal increase of both IL6 and CRP in the saliva of

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Publication Date
Fri Jan 01 2021
Journal Name
International Journal Of Agricultural And Statistical Sciences
USE OF MODIFIED MAXIMUM LIKELIHOOD METHOD TO ESTIMATE PARAMETERS OF THE MULTIPLE LINEAR REGRESSION MODEL
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Publication Date
Fri Oct 11 2019
Journal Name
Journal Of The College Of Education For Women
The South Baghdad Electrical Station and its Environmental Impact on the Tigris River
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The South Baghdad electrical station located on the eastern bank of the Tigris River south of Baghdad city was selected within the municipality of Karrada between  two  latitude ( 3315 , 33 0  18 )North  and  longitude ( 44 0  27 , 44 030 ) East . The purpose of the study is to determine the contribution of the station to the effect of pollution of the Tigris water by taking water samples at the station site and two sites, one before and the other after the station, distributed over time periods of three months between each sample of water and the beginning of August and November Shabat and Mayar and analyzed water samples physically, chemically and biologic

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Publication Date
Sat Dec 30 2023
Journal Name
Iraqi Journal Of Science
Modeling of Sediment Transport Around A Water Intake In Tigris River – Baghdad Using A Numerical 2d Model
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     Understanding sedimentation behavior and its transport capacity in the Tigris River is of significant importance owing to the detrimental consequences caused by it. This study investigates the sediment amounts transported along the reach of the Tigris River in Baghdad. The CCHE2D model which is a common tool developed by the National Center for Computational Hydrological Science and Engineering (NCCHE) was applied to investigate the flow pattern and sediment amounts within 7 km reach. The model was initially calibrated and validated under steady-state conditions at the Sarai gauging station (upstream) and its performance was evaluated around the Abu Nawas water treatment plant (downstream). The result shows that the water surfac

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Publication Date
Thu Oct 31 2019
Journal Name
Journal Of Engineering And Applied Sciences
Comparison of Estimate Methods of Multiple Linear Regression Model with Auto-Correlated Errors when the Error Distributed with General Logistic
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In this research, we studied the multiple linear regression models for two variables in the presence of the autocorrelation problem for the error term observations and when the error is distributed with general logistic distribution. The auto regression model is involved in the studying and analyzing of the relationship between the variables, and through this relationship, the forecasting is completed with the variables as values. A simulation technique is used for comparison methods depending on the mean square error criteria in where the estimation methods that were used are (Generalized Least Squares, M Robust, and Laplace), and for different sizes of samples (20, 40, 60, 80, 100, 120). The M robust method is demonstrated the best metho

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Publication Date
Thu Oct 31 2019
Journal Name
Journal Of Engineering And Applied Sciences
Comparison of Estimate Methods of Multiple Linear Regression Model with Auto-Correlated Errors when the Error Distributed with General Logistic
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In this research, we studied the multiple linear regression models for two variables in the presence of the autocorrelation problem for the error term observations and when the error is distributed with general logistic distribution. The auto regression model is involved in the studying and analyzing of the relationship between the variables, and through this relationship, the forecasting is completed with the variables as values. A simulation technique is used for comparison methods depending

Publication Date
Wed Jan 23 2019
Journal Name
Natural Resources Research
Tigris River Sediments as Abrasive for Polishing Marble
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Publication Date
Sun Apr 01 2018
Journal Name
Journal Of Economics And Administrative Sciences
Estimate Kernel Ridge Regression Function in Multiple Regression
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             In general, researchers and statisticians in particular have been usually used non-parametric regression models when the parametric methods failed to fulfillment their aim to analyze the models  precisely. In this case the parametic methods are useless so they turn to non-parametric methods for its easiness in programming. Non-parametric methods can also used to assume the parametric regression model for subsequent use. Moreover, as an advantage of using non-parametric methods is to solve the problem of Multi-Colinearity between explanatory variables combined with nonlinear data. This problem can be solved by using kernel ridge regression which depend o

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Publication Date
Wed Mar 30 2022
Journal Name
Journal Of Economics And Administrative Sciences
Using Quadratic Form Ratio Multiple Test to Estimate Linear Regression Model Parameters in Big Data with Application: Child Labor in Iraq
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              The current paper proposes a new estimator for the linear regression model parameters under Big Data circumstances.  From the diversity of Big Data variables comes many challenges that  can be interesting to the  researchers who try their best to find new and novel methods to estimate the parameters of linear regression model. Data has been collected by Central Statistical Organization IRAQ, and the child labor in Iraq has been chosen as data. Child labor is the most vital phenomena that both society and education are suffering from and it affects the future of our next generation. Two methods have been selected to estimate the parameter

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