يُمثل التوسع الحضري أحد أبرز التحولات الديموغرافية العالمية. وفي العراق، يكتسب هذا التحول بُعداً استثنائياً نتيجة للتغيرات السياسية، والأمنية، والاقتصادية الجذرية الممتدة من عام 1997 وحتى 2025، والتي أفرزت تبايناً مكانياً حاداً في مسارات النمو السكاني بين المراكز الحضرية. استندت هذه الدراسة إلى تحليل بيانات رسمية لـ 12 مركزاً حضرياً رئيسياً. وكشفت النتائج عن فجوة ديموغرافية واسعة؛ إذ برزت مدن إقليم كردستان (السليمانية وأربيل) كأقطاب للنمو الاستثنائي، في حين سجلت مدن أخرى معدلات بطيئة. وأظهر "تحليل البؤر الساخنة" (Hot Spot Analysis) غياب أي تجمعات مكانية ذات دلالة إحصائية، مما يؤكد أن أنماط النمو المرتفعة هي ظواهر محلية مدفوعة بديناميكيات خاصة بكل مدينة، وليست تمدداً إقليمياً. علاوة على ذلك، أثبت نموذج الانحدار الموزون جغرافياً (GWR) التباين المكاني لتأثير العوامل المستقلة؛ حيث لعب توفر الاستقرار الأمني دوراً حاسماً كعامل جذب قوي في مدن كالسليمانية متجاوزاً تأثير المتغيرات الاقتصادية. وتخلص الدراسة إلى أن السياسات التخطيطية الحضرية الموحدة غير فعالة في السياق العراقي، موصيةً بضرورة تبني استراتيجيات مكانية مخصصة تستجيب لمتطلبات التنمية المتفاوتة لكل منطقة على حدة.
In this paper, the process of comparison between the tree regression model and the negative binomial regression. As these models included two types of statistical methods represented by the first type "non parameter statistic" which is the tree regression that aims to divide the data set into subgroups, and the second type is the "parameter statistic" of negative binomial regression, which is usually used when dealing with medical data, especially when dealing with large sample sizes. Comparison of these methods according to the average mean squares error (MSE) and using the simulation of the experiment and taking different sample
... Show MoreLinear discriminant analysis and logistic regression are the most widely used in multivariate statistical methods for analysis of data with categorical outcome variables .Both of them are appropriate for the development of linear classification models .linear discriminant analysis has been that the data of explanatory variables must be distributed multivariate normal distribution. While logistic regression no assumptions on the distribution of the explanatory data. Hence ,It is assumed that logistic regression is the more flexible and more robust method in case of violations of these assumptions.
In this paper we have been focus for the comparison between three forms for classification data belongs
... Show Morethe research goal is preparing a list of standard criteria and quality controls for information technology applications to serve the Holy Quran.
To achieve this goal, the researcher has built a list of criteria according to the following steps:
First - identify the key areas covered by the whole list which are:
1 – Standards of system building and implementing with the operating screens.
2 – Standards of display forms including audio and video presentation.
3 – Standards which are related to the program philosophy.
4 - Standards which are related to the program objectives.
... Show MoreJalal Jaafar Al-Awqati is a military figure who played a significant role in developing the Iraqi Air Force after the July 14, 1958 revolution. His personality crystallized during his studies. He was a thoughtful person, a good communicator, a man of few words, and held firm principled positions. He was known for his unique way of thinking. He was not violent in his daily dealings. He believed in democracy as a political doctrine and saw it as the best means and sure guarantee for solving the country's problems.
Regression testing being expensive, requires optimization notion. Typically, the optimization of test cases results in selecting a reduced set or subset of test cases or prioritizing the test cases to detect potential faults at an earlier phase. Many former studies revealed the heuristic-dependent mechanism to attain optimality while reducing or prioritizing test cases. Nevertheless, those studies were deprived of systematic procedures to manage tied test cases issue. Moreover, evolutionary algorithms such as the genetic process often help in depleting test cases, together with a concurrent decrease in computational runtime. However, when examining the fault detection capacity along with other parameters, is required, the method falls sh
... Show MoreThe logistic regression model regarded as the important regression Models ,where of the most interesting subjects in recent studies due to taking character more advanced in the process of statistical analysis .
The ordinary estimating methods is failed in dealing with data that consist of the presence of outlier values and hence on the absence of such that have undesirable effect on the result. &nbs
... Show MoreThe 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
... Show MoreAbstract
The research examined with the importance banking merger to address the situation of Troubled banks in Iraq, Through The use of Logistic Regression Model. . The study attempted to present a conceptual aspect of banking merger and logistic regression, as well as the applied aspect which includes a sample consisting of six private Iraqi banks, and the hypothesis of the study is that the promotion of mergers among banks has positive impacts on improving the efficiency of performance of troubled banks, which contributes to the increase of banking services, raise of their financial indicators and the high liquidity and profits of the new banking entity as it is a way to overcome the prevailing banking crises.
... Show MoreIn this paper, previous studies about Fuzzy regression had been presented. The fuzzy regression is a generalization of the traditional regression model that formulates a fuzzy environment's relationship to independent and dependent variables. All this can be introduced by non-parametric model, as well as a semi-parametric model. Moreover, results obtained from the previous studies and their conclusions were put forward in this context. So, we suggest a novel method of estimation via new weights instead of the old weights and introduce
Paper Type: Review article.
another suggestion based on artificial neural networks.