An optimization analysis of drilling process constitutes a powerful tool for operating under desired pressure levels and simultaneously maximizing the penetration rate, which reduces costs and time thus increases the profit.
In this study, a composite drilling model (Young-Bourgyen model) of eight functions was used to determine the optimum drilling mechanical parameters (Weight on bit and rotary speed) for an Iraqi oil field. These functions model the effect of most drilling parameters such as formation strength, mud density, formation compaction, weight on bit, rotary speed, tooth dullness, and bit hydraulic on drilling rate. Data are extracted from bit record and drilling report of well BUZ-20 for calculation of eight exponents of

The Purpose of this research is a comparison between two types of multivariate GARCH models BEKK and DVECH to forecast using financial time series which are the series of daily Iraqi dinar exchange rate with dollar, the global daily of Oil price with dollar and the global daily of gold price with dollar for the period from 01/01/2014 till 01/01/2016.The estimation, testing and forecasting process has been computed through the program RATS. Three time series have been transferred to the three asset returns to get the Stationarity, some tests were conducted including Ljung- Box, Multivariate Q and Multivariate ARCH to Returns Series and Residuals Series for both models with comparison between the estimation and for

In this research was the study of a single method of estimation and testing parameters mediating variables (Mediation) in a specimen structural equations SEM a bootstrap method, for the purpose of application of the integrated survey of the situation Marital data and health mirror Iraqi (I-WISH) for the year 2011 from the Ministry of Planning - device Central Bureau of Statistics, and applied to the appropriate data from the terms of the data to a form of structural equation SEM using factor analysis affirmative (Confirmatory Factor analysis) CFA As a way to see the match variables that make up the model, and after confirming the model matching or suitability are having the effect of variables mediation in the model tested by the

Linear 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

Semi-parametric models analysis is one of the most interesting subjects in recent studies due to give an efficient model estimation. The problem when the response variable has one of two values either 0 ( no response) or one – with response which is called the logistic regression model.

We compare two methods Bayesian and . Then the results were compared using MSe criteria.

A simulation had been used to study the empirical behavior for the Logistic model , with  different sample sizes and variances. The results using represent that the Bayesian method is better than the   at small samples sizes.

<span>Dust is a common cause of health risks and also a cause of climate change, one of the most threatening problems to humans. In the recent decade, climate change in Iraq, typified by increased droughts and deserts, has generated numerous environmental issues. This study forecasts dust in five central Iraqi districts using machine learning and five regression algorithm supervised learning system framework. It was assessed using an Iraqi meteorological organization and seismology (IMOS) dataset. Simulation results show that the gradient boosting regressor (GBR) has a mean square error of 8.345 and a total accuracy ratio of 91.65%. Moreover, the results show that the decision tree (DT), where the mean square error is 8.965, c

Abstract:
       This study is studied one method of estimation and testing parameters mediating variables in a structural equations model SEM is causal steps method, in order to identify and know the variables that have indirect effects by estimating and testing mediation variables parameters by the above way and then applied to Iraq Women Integrated Social and Health Survey (I-WISH) for year 2011 from the Ministry of planning - Central statistical organization to identify if the  variables having the effect of mediation in the model by the step causal methods by using AMOS program V.23, it was the independent variable X represents a phenomenon studied (cultural case of the

The aim of this paper to find Bayes estimator under new loss function assemble between symmetric and asymmetric loss functions, namely, proposed entropy loss function, where this function that merge between entropy loss function and the squared Log error Loss function, which is quite asymmetric in nature. then comparison a the Bayes estimators of exponential distribution under the proposed function, whoever, loss functions ingredient for the proposed function the using a standard mean square error (MSE) and Bias quantity (M_bias), where the generation of the random data using the simulation for estimate exponential distribution parameters different sample sizes (n=10,50,100) and (N=1000), taking initial

This paper includes the application of Queuing theory with of Particle swarm algorithm or is called (Intelligence swarm) to solve the problem of The queues and developed for General commission for taxes /branch Karkh center in the service stage of the Department of calculators composed of six  employees , and it was chosen queuing model is a single-service channel  M / M / 1 according to the nature of the circuit work mentioned above and it will be divided according to the letters system for each employee, and  it was composed of data collection times (arrival time , service time, departure time)