In this study, we focused on the random coefficient estimation of the general regression and Swamy models of panel data. By using this type of data, the data give a better chance of obtaining a better method and better indicators. Entropy's methods have been used to estimate random coefficients for the general regression and Swamy of the panel data which were presented in two ways: the first represents the maximum dual Entropy and the second is general maximum Entropy in which a comparison between them have been done by using simulation to choose the optimal methods.

The results have been compared by using mean squares error and mean absolute percentage error to different cases in term of correlation valu

تناول البحث نموذج الانحدار الذاتي الحيزي ونموذج الخطأ الحيزي في محاولة لتقديم دليل عملي يوضح اهمية التحليل الحيزي، مع التركيز بصفة خاصة على أهمية استعمال نماذج الانحدار الحيزي والتي تضم كل منها الاعتمادية الحيزية التي يتم أختبار وجودها من عدمه بأختبار موران، وان تجاهل  هذه الاعتمادية قد يؤدي إلى ضياع معلومات مهمة عن تلك الظاهرة تنعكس في نهاية المطاف على قوة تقدير المؤشر الإحصائي المستخرج، تعدّ هذه الن

تناول البحث نموذج الانحدار الذاتي الحيزي ونموذج الخطأ الحيزي في محاولة لتقديم دليل عملي يوضح اهمية التحليل الحيزي، مع التركيز بصفة خاصة على أهمية استعمال نماذج الانحدار الحيزي والتي تضم كل منها الاعتمادية الحيزية التي يتم أختبار وجودها من عدمه بأختبار موران، وان تجاهل  هذه الاعتمادية قد يؤدي إلى ضياع معلومات مهمة عن تلك الظاهرة تنعكس في نهاية المطاف على قوة تقدير المؤشر الإحصائي المستخرج، تعدّ هذه الن

The research discusses the need to find the innovative structures and methodologies for developing Human Capital (HC) in Iraqi Universities. One of the most important of these structures is Communities of Practice (CoPs) which contributes to develop HC by using learning, teaching and training through the conversion speed of knowledge and creativity into practice. This research has been used the comparative approach through employing the methodology of Data Envelopment Analysis (DEA) by using (Excel 2010 - Solver) as a field evidence to prove the role of CoPs in developing HC. In light of the given information, a researcher adopted on an archived preliminary data about (23) colleges at Mosul University as a deliberate sample for t

In this paper new methods were presented based on technique of differences which is the difference- based modified jackknifed generalized ridge regression estimator(DMJGR) and difference-based generalized  jackknifed ridge regression estimator(DGJR), in estimating the parameters of linear part of the partially linear model. As for the nonlinear part represented by the nonparametric function, it was estimated using Nadaraya Watson smoother. The partially linear model was compared using these proposed methods with other estimators based on differencing technique through the MSE comparison criterion in simulation study.

         The availability of statistical data plays an important role in planning process. The importance of this research which deals with safety of statistical data from errors and outliers values. The Objective of this study is to determine the outlier values in statistical data by using modern exploratory data methods and comparing them with parametric methods. The research has been divided into four chapters ,the main important conclusions reached are:1-The exploratory methods and the parametric methods showed variation between them in determining the outlier values in the data.

2-The study showed that the box plot method was the best method used in determining

The goal beyond this Research is to review methods that used to estimate Logistic distribution parameters. An exact estimators method which is the Moment method, compared with other approximate estimators obtained essentially from White approach such as: OLS, Ridge, and Adjusted Ridge as a suggested one to be applied with this distribution. The Results of all those methods are based on Simulation experiment, with different models and variety of  sample sizes. The comparison had been made with respect to two criteria: Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE).  

Multilevel models are among the most important models widely used in the application and analysis of data that are characterized by the fact that observations take a hierarchical form, In our research we examined the multilevel logistic regression model (intercept random and slope random model) , here the importance of the research highlights that the usual regression models calculate the total variance of the model and its inability to read variance and variations between levels ,however in the case of multi-level regression models, the calculation of  the total variance is inaccurate and therefore these models calculate the variations for each level of the model, Where the research aims to estimate the parameters of this m

Estimation of the unknown parameters in 2-D sinusoidal signal model can be considered as important and difficult problem. Due to the difficulty to find estimate of all the parameters of this type of models at the same time, we propose sequential non-liner least squares method and sequential robust  M method after their development through the use of sequential  approach in the estimate suggested by Prasad et al to estimate unknown frequencies and amplitudes for the 2-D sinusoidal compounds but depending on Downhill Simplex Algorithm in solving non-linear equations for the purpose of obtaining non-linear parameters estimation which represents frequencies and then use of least squares formula to estimate