The use of Bayesian approach has the promise of features indicative of regression analysis model classification tree to take advantage of the above information by, and ensemble trees for explanatory variables are all together and at every stage on the other. In addition to obtaining the subsequent information at each node in the construction of these classification tree. Although bayesian estimates is generally accurate, but it seems that the logistic model is still a good competitor in the field of binary responses through its flexibility and mathematical representation. So is the use of three research methods data processing is carried out, namely: logistic model, and model classification regression tree, and bayesian regression tree mode

The groundwater represents the main source of water in the study area due to lack of surface water. The Dammam unconfined aquifer represents the main aquifer in the study area and Southern desert because of the regional extent, the quantity and quality of water. Many groundwater wells have been drilled in the study area to coverage the huge demand of water for agricultural purposes. The Geographic Information System (GIS) was used to estimate the volume of water which calculated (25.6964 × 109 m3) within the study area , automate calculation of the area of Al Salman basin using digital elevation models, derive the thickness maps of Al
Dammam unconfined aquifer from Key holes (KH) and Bore holes (

   The technology of reducing dimensions and choosing variables are very important topics in statistical analysis to multivariate. When two or more of the predictor variables are linked in the complete or incomplete regression relationships, a problem of multicollinearity are occurred which consist of the breach of one basic assumptions of the ordinary least squares method with incorrect estimates results.

 There are several methods proposed to address this problem, including the partial least squares (PLS), used to reduce dimensional regression analysis. By using linear transformations that convert a set of variables associated with a high link to a set of new independent variables and unr

The present study intends to estimate the surface runoff and groundwater
recharge in Lialan basin using the soil conservation service method (curve number)
and chloride mass balance method (CMB) respectively. Lialan basin is located at the
southeast part of Kirkuk governorate, between longitudes (44° 21' 00" E - 44° 42'
00" E) and latitudes (35° 7' 30" N - 35° 28' 30" N) , which covering an area of about
436 km2 and contains many geological formations. In this study, based on the
annual rainfall data extending from (1970 - 2016) obtained from the Kirkuk
meteorological station, the surface runoff was calculated using the curve number
method (CN), and found to be equal to 90.4 mm/year, which represent 26.07 % of

            In this paper the research represents an attempt of expansion in using the parametric and non-parametric estimators to estimate the median effective dose ( ED50 ) in the quintal bioassay and comparing between  these methods . We have Chosen three estimators for Comparison. The first estimator is
( Spearman-Karber )  and the second estimator is ( Moving Average ) and The Third estimator  is ( Extreme Effective Dose ) . We used a minimize Chi-square as a parametric method. We made a Comparison for these estimators by calculating the mean square error of (ED50) for each one of them and comparing it with the optimal the mean square

Abstract:

 This research aims to compare Bayesian Method and Full Maximum Likelihood to estimate hierarchical Poisson regression model.

The comparison was done by  simulation  using different sample sizes (n = 30, 60, 120) and different Frequencies (r = 1000, 5000) for the experiments as was the adoption of the  Mean Square Error to compare the preference estimation methods and then choose the best way to appreciate model and concluded that hierarchical Poisson regression model that has been appreciated Full Maximum Likelihood Full Maximum Likelihood  with sample size  (n = 30) is the best to represent the maternal mortality data after it has been reliance value param

Abstract

  The logistic regression model is one of the nonlinear models that aims at obtaining highly efficient capabilities, It also the researcher an idea of the effect of the explanatory variable on the binary response variable.                                                                                  &nb

Abstract:

The models of time series often suffer from the problem of the existence of outliers ​​that accompany the data collection process for many reasons, their existence may have a significant impact on the estimation of the parameters of the studied model. Access to highly efficient estimators  is one of the most important stages of statistical analysis, And it is therefore important to choose the appropriate methods to obtain good  estimators. The aim of this research is to compare the ordinary estimators and the robust estimators of the estimation of the parameters of

Abstract

In this research provide theoretical aspects of one of the most important statistical distributions which it is Lomax, which has many applications in several areas, set of estimation methods was used(MLE,LSE,GWPM) and compare with (RRE) estimation method ,in order to find out best estimation method set of simulation experiment (36) with many replications  in order  to get mean square error and used it to make compare , simulation experiment  contrast with (estimation method, sample size ,value of location and shape parameter) results show that estimation method effected by simulation experiment factors and ability of using other estimation methods such as(Shrinkage, jackknif

The logistic regression model is an important statistical model showing the relationship between the binary variable and the explanatory variables.                                                        The large number of explanations that are usually used to illustrate the response led to the emergence of the problem of linear multiplicity between the explanatory variables that make estimating the parameters of the model not accurate.