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Using count regression models to investigate the most important economic factors affecting divorce in Iraq
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The two most popular models inwell-known count regression models are Poisson and negative binomial regression models. Poisson regression is a generalized linear model form of regression analysis used to model count data and contingency tables. Poisson regression assumes the response variable Y has a Poisson distribution, and assumes the logarithm of its expected value can be modeled by a linear combination of unknown parameters. Negative binomial regression is similar to regular multiple regression except that the dependent (Y) variables an observed count that follows the negative binomial distribution. This research studies some factors affecting divorce using Poisson and negative binomial regression models. The factors are unemployment rate, inflation and Gini coefficient. The data were taken from the website of the Statistics Center for the years 2002 to 2023.Under the Poisson regression model, each factor has been reported to have an effect on the divorce rate. The two factors of inflation and unemployment had a direct effect and income inequality factors had an inverse effect on the divorce rate. But, under the negative binomial regression model, only inflation has an effect on the number of divorces. It is worth noting that according to the AIC values, the negative binomial regression model has a better fit than the Poisson regression model because its AIC value is lower.

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Publication Date
Tue Oct 22 2024
Journal Name
Iraqi Statisticians Journal
Inferential Methods for the Dagum Regression Model
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The Dagum Regression Model, introduced to address limitations in traditional econometric models, provides enhanced flexibility for analyzing data characterized by heavy tails and asymmetry, which is common in income and wealth distributions. This paper develops and applies the Dagum model, demonstrating its advantages over other distributions such as the Log-Normal and Gamma distributions. The model's parameters are estimated using Maximum Likelihood Estimation (MLE) and the Method of Moments (MoM). A simulation study evaluates both methods' performance across various sample sizes, showing that MoM tends to offer more robust and precise estimates, particularly in small samples. These findings provide valuable insights into the ana

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Publication Date
Sun Jun 07 2015
Journal Name
Baghdad Science Journal
A comparative study between conventional methods and Vidas UP Salmonella (SPT) to investigate salmonella species from local and imported meat
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The study was preformed for investigating of Salmonella from meat, and compared Vidas UP Salmonella (SPT) with the traditional methods of isolation for Salmonella , were examined 42 meat samples (Beef and Chicken) from the Local and Imported From local markets in the city of Baghdad from period December 2013 -February 2014 the samples were cultured on enrichment and differential media and examined samples Vidas, and confirmed of isolates by cultivation chromgenic agar, biochemical tests ,Api20 E systeme , In addition serological tests , and the serotypes determinate in the Central Public Health Laboratory / National Institute of Salmonella The results showed the contamination in imported meat was more than in the local meat 11.9% and 2

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Publication Date
Wed Nov 05 2025
Journal Name
Irrigation And Drainage
Predicting Potential Salinity in River Water for Irrigation Water Purposes Using Integrative Machine Learning Models
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ABSTRACT<p>Accurate prediction of river water quality parameters is essential for environmental protection and sustainable agricultural resource management. This study presents a novel framework for estimating potential salinity in river water in arid and semi‐arid regions by integrating a kernel extreme learning machine (KELM) with a boosted salp swarm algorithm based on differential evolution (KELM‐BSSADE). A dataset of 336 samples, including bicarbonate, calcium, pH, total dissolved solids and sodium adsorption ratio, was collected from the Idenak station in Iran and was used for the modelling. Results demonstrated that KELM‐BSSADE outperformed models such as deep random vector funct</p> ... Show More
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Publication Date
Thu Nov 01 2018
Journal Name
International Journal Of Science And Research (ij
Mathematical Models for Predicting of Organic and Inorganic Pollutants in Diyala River Using AnalysisNeural Network
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Diyala river is the most important tributaries in Iraq, this river suffering from pollution, therefore, this research aimed to predict organic pollutants that represented by biological oxygen demand BOD, and inorganic pollutants that represented by total dissolved solids TDS for Diyala river in Iraq, the data used in this research were collected for the period from 2011-2016 for the last station in the river known as D17, before the river meeting Tigris river in Baghdad city. Analysis Neural Network ANN was used in order to find the mathematical models, the parameters used to predict BOD were seven parameters EC, Alk, Cl, K, TH, NO3, DO, after removing the less importance parameters. While the parameters that used to predict TDS were fourte

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Publication Date
Sun Jul 09 2023
Journal Name
Journal Of Engineering
Paving the Way for PPP's to Infrastructure Projects in Iraq
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The political situation experienced by Iraq before the events of 2003 that led to the collapse of infrastructure. rebuilding costs were estimated after 2003 by187(million USD) according to the estimates of the basic needs as stated in Five-Year Plan 2010-2014. The difficult in financing projects and the continuous demands for maintenance and operating cost, and working by contemporary styles in different countries, the strategic option is to adopt the government entering the private sector as a partner in the development process. Since public _private partnership (PPP's) is at a germinating stage of development in Iraq, it has been studied the critical success factors(CSF's) in the experiences of countries that have implemented the style

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Publication Date
Wed May 02 2012
Journal Name
Journal Of Planner And Development
the population in Iraq up to 2035 an analytical study
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The population studies are one of the difficult tasks facing the world in all periods. most of the researchers and who have relationship to population policies and development plans, may have succumbed to the idea that the population problem is based and confined mainly in the rate of increase in population, or the so – called population explosion, and not the content because of its pressure on resources and there is no problem of population if the resources are available and therefore no need for the development and implementation of population policies in any way . While the population policies here should take a range of general and comprehensive in every respect to population and demographic phenomena distribution, not only

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Publication Date
Tue Sep 11 2018
Journal Name
Iraqi Journal Of Physics
Analytical study of high absorption region of the absorption edge of a-Si:H using nonlinear regression method
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This research is concerned with the re-analysis of optical data (the imaginary part of the dielectric function as a function of photon energy E) of a-Si:H films prepared by Jackson et al. and Ferlauto et al. through using nonlinear regression fitting we estimated the optical energy gap and the deviation from the Tauc model by considering the parameter of energy photon-dependence of the momentum matrix element of the p as a free parameter by assuming that density of states distribution to be a square root function. It is observed for films prepared by Jackson et al. that the value of the parameter p for the photon energy range is is close to the value assumed by the Cody model and the optical gap energy is which is also close to the value

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Publication Date
Sat Feb 14 2026
Journal Name
Architecture Image Studies
The role of artificial intelligence in developing humanities studies from archaeological discovery to historical recording and geographical analysis: Application models
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The Fourth Industrial Revolution represents an advanced stage of technological development, characterized by the integration of digital, physical, and biological technologies, with a strong focus on smart connectivity and advanced data analysis. At the core of this revolution stands Artificial Intelligence (AI), which enables the processing of vast amounts of data, decision-making with speed and accuracy, automation of processes, and enhancement of productivity and quality. This research examines the transformative role of AI in the humanities, particularly in archaeological, historical, and geographical studies, where traditional methods face limitations in handling complex and extensive datasets.The study aims to highlight these l

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Crossref
Publication Date
Sun May 11 2025
Journal Name
Iraqi Statisticians Journal
Estimating General Linear Regression Model of Big Data by Using Multiple Test Technique
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Publication Date
Sat Dec 31 2022
Journal Name
Journal Of Economics And Administrative Sciences
Seemingly Unrelated Regression Model to Measure the Profitability of Some Iraqi Private Commercial Banks with Presence of Outliers
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A seemingly uncorrelated regression (SUR) model is a special case of multivariate models, in which the error terms in these equations are contemporaneously related. The method estimator (GLS) is efficient because it takes into account the covariance structure of errors, but it is also very sensitive to outliers. The robust SUR estimator can dealing outliers. We propose two robust methods for calculating the estimator, which are (S-Estimations, and FastSUR). We find that it significantly improved the quality of SUR model estimates. In addition, the results gave the FastSUR method superiority over the S method in dealing with outliers contained in the data set, as it has lower (MSE and RMSE) and higher (R-Squared and R-Square Adjus

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