The problem of Multicollinearity is one of the most common problems, which deal to a large extent with the internal correlation between explanatory variables. This problem is especially Appear in economics and applied research, The problem of Multicollinearity has a negative effect on the regression model, such as oversized variance degree and estimation of parameters that are unstable when we use the Least Square Method ( OLS), Therefore, other methods were used to estimate the parameters of the negative binomial model, including the estimated Ridge Regression Method and the Liu type estimator, The negative binomial regression model is a nonline

ان الهدف من الدراسة هو بيان القدرة التنبؤية الافضل بين انموذج الانحدار اللوجستي والدالة المميزة الخطية باستعمال البيانات الاصليه اولا ثم المركبات الرئيسة لتقليص الابعاد بين المتغيرات لبيانات المسح الاجتماعي والاقتصادي للاسرة لمحافظة بغداد لعام 2012 وتضمنت عينة البحث 615 مفردة لـ13 متغير، 12منها متغير توضيحي والمتغير المعتمد شمل العاملين والعاطلين عن العمل، تم اجراء المقارنة بين الطريقتين اعلاه واتضح من خلال

تعد النماذج متعددة المستويات من اهم النماذج الواسعة الاستعمال في تطبيق وتحليل البيانات التي تتصف بكون المشاهدات تتخذ الشكل الهرمي، وفي بحثنا هذا تم تناول انموذج الانحدار اللوجستي الثنائي متعدد المستويات (انموذج حد ثابت عشوائي وميل عشوائي )، هنا تبرز اهمية البحث ان نماذج الانحدار الاعتيادية تقوم بحساب التباين العام للأنموذج وعدم امكانيتها قراءة التباينات والتغيرات بين المستويات لكن في حالة نماذج الانح

Transportation problems are considered as a type of operation research problems. In fact, they deal with scheduling transportation of goods from their source to delivery sites in the minimum cost.

Such problems can be solved by the available traditional methods, which include; North-West corner, Least cost and Vogel’s method. As well as if this transportation problem is considered as a linear program it can also be solved by using Simplex method

The goal of the present study is to compare different research methods to provide the optimal and minimum cost.

This study was applied to resolve a transportation problem related to land Transportation Company, w

Abstract

The Classical Normal Linear Regression Model Based on Several hypotheses, one of them is Heteroscedasticity as it is known that the wing of least squares method (OLS), under the existence of these two problems make the estimators, lose their desirable properties, in addition the statistical inference becomes unaccepted table. According that we put tow alternative,  the first one is  (Generalized Least Square) Which is denoted by (GLS), and the second alternative is to (Robust covariance matrix estimation) the estimated parameters method(OLS), and that the way (GLS) method neat and certified, if the capabilities (Efficient) and the statistical inference Thread on the basis of an acceptable

تم إستعراض تقدير معلمتي التوزيع اللوجستي بإستعمال طريقة ذات مقدرات مضبوطة وهي طريقة العزوم، ومقارنتها بمقدرات تقريبية مأخوذة بالأساس من أسلوب طريقة (وايت) في التقدير بأعتبار التوزيع اللوجستي من التوزيعات الأحتمالية الأسية، وهي كل من طريقة المربعات الصغرى الأعتيادية، وطريقة أنحدار الحرف، وأقتراح تطبيق طريقة أنحدار الحرف المعدلة على هذا التوزيع. وتم أستحصال النتائج بالأستناد الى تجارب محاكاة لتلك الطر

In this research, the covariance estimates were used to estimate the population mean in the stratified random sampling and combined regression estimates. were compared by employing the robust variance-covariance matrices estimates with combined regression estimates by employing the traditional variance-covariance matrices estimates when estimating the regression parameter, through the two efficiency criteria (RE) and mean squared error (MSE). We found that robust estimates significantly improved the quality of combined regression estimates by reducing the effect of outliers using robust covariance and covariance matrices estimates (MCD, MVE) when estimating the regression parameter. In addition, the results of the simulation study proved

       In recent years, the attention of researchers has increased of semi-parametric regression models, because it is possible to integrate the parametric and non-parametric regression models in one and then form a regression model has the potential to deal with the cruse of dimensionality in non-parametric models that occurs through the increasing of explanatory variables. Involved in the analysis and then decreasing the accuracy of the estimation. As well as the privilege of this type of model with flexibility in the application field compared to the parametric models which comply with certain conditions such as knowledge of the distribution of errors or the parametric models may

This research introduce a study with application on Principal Component Regression obtained from some of the explainatory variables to limitate Multicollinearity problem among these variables and gain staibilty in their estimations more than those which yield from Ordinary Least Squares. But the cost that we pay in the other hand losing a little power of the estimation of the predictive regression function in explaining the essential variations. A suggested numerical formula has been proposed and applied by the researchers as optimal solution, and vererifing the its efficiency by a program written by the researchers themselves for this porpuse through some creterions: Cumulative Percentage Variance, Coefficient of Determination, Variance