Abstract

Characterized by the Ordinary Least Squares (OLS) on Maximum Likelihood for the greatest possible way that the exact moments are known , which means that it can be found, while the other method they are unknown, but approximations to their biases correct to 0(n^-1) can be obtained by standard methods. In our research expressions for approximations to the biases of the ML estimators (the regression coefficients and scale parameter) for linear (type 1) Extreme Value Regression Model for Largest Values are presented by using the advanced approach depends on finding the first derivative, second and third.

A seasonal study of periphytic algae attached to the surface of river boats was conducted in Tigris river in Al Aadhamiya site for the period from October 2016 to May 2017. A total of 107 taxa of periphytic algae were identified belonging to the four classes of algae. The periphytic algae community dominated by Bacillariophyceae was (60.7%) followed by Chlorophyceae (20.5%) and Cyanophyceae (17.7%) Chrysophyceae was constituted (0.9%) of the total number. During the whole period of study filamentous taxa such as Oscillatoria amphibian, Phormidium spp., Spirulinagigantean, Cladophoreglomerata and Melosira roeseana remained the dominant colonizer which may be reflect the ability of this species to grow multiplies under different environmental

    In the presence of multi-collinearity problem, the parameter estimation method based on the ordinary least squares procedure is unsatisfactory. In 1970, Hoerl and Kennard insert analternative method labeled as estimator of ridge regression.

In such estimator, ridge parameter plays an important role in estimation. Various methods were proposed by many statisticians to select the biasing constant (ridge parameter). Another popular method that is used to deal with the multi-collinearity problem is the principal component method. In this paper,we employ the simulation technique to compare the performance of principal component estimator with some types of ordinary ridge regression estimators based on the value of t

Pollution of the aquatic environment and the depletion of the natural resource cause imbalance in the natural balance of the river environment and contributes to the deterioration of life and the killing of living organisms. Most of the old and modern cities and urban centers were set up close to the rivers because water enters the main lifeblood and all its facilities. The proximity of cities to rivers caused environmental problems resulting from the dumping of residues of these cities to a large and continuous, these wastes include all uses of the city (industrial, agricultural, residential and commercial) and others. The accumulation of these wastes inside the rivers water kills life and makes them unsuitable for various uses to bury

Everybody is connected with social media like (Facebook, Twitter, LinkedIn, Instagram…etc.) that generate a large quantity of data and which traditional applications are inadequate to process. Social media are regarded as an important platform for sharing information, opinion, and knowledge of many subscribers. These basic media attribute Big data also to many issues, such as data collection, storage, moving, updating, reviewing, posting, scanning, visualization, Data protection, etc. To deal with all these problems, this is a need for an adequate system that not just prepares the details, but also provides meaningful analysis to take advantage of the difficult situations, relevant to business, proper decision, Health, social media, sc

 A simulation study is used to examine the robustness of some estimators on a multiple linear regression model with problems of multicollinearity and non-normal errors, the Ordinary least Squares (LS) ,Ridge Regression, Ridge Least Absolute Value (RLAV), Weighted Ridge (WRID), MM and a robust ridge regression estimator MM estimator, which denoted as RMM this is the modification of the Ridge regression by incorporating robust MM estimator . finialy, we show that RMM is the best among the other estimators

 The logistic regression model of the most important regression models a non-linear which aim getting estimators have a high of efficiency, taking character more advanced in the process of statistical analysis for being a models appropriate form of Binary Data.                                                          

Among the problems that appear as a result of the use of some statistical methods I

This research aims to study the methods of reduction of dimensions that overcome the problem curse of dimensionality when traditional methods fail to provide a good estimation of the parameters So this problem must be dealt with directly . Two methods were used to solve the problem of high dimensional data, The first method is the non-classical method Slice inverse regression ( SIR ) method and the proposed weight standard Sir (WSIR) method and principal components (PCA) which is the general method used in reducing dimensions,    (SIR ) and (PCA) is based on the work of linear combinations of a subset of the original explanatory variables, which may suffer from the problem of heterogeneity and the problem of linear

Sustainability including renewable energy and green power, is one of the important feature in recent years due to environmental constraints and the emission of CO2 from fossil fuel. Pressure retarded osmosis (PRO) process is considered one of the effective technology for power generation. This study assessed the application of pressure retarded osmosis to produce power from Tigris River water in Baghdad City, Iraq. Spiral wound TFC membrane was tested in the PRO process with different variables. The effect of different types of draw solutions (MgCl2, NaCl, Sodium Formate, KCl, Sodium Acetate), applied pressure (0 – 7 bar), and draw solution concentration (0.08 and 0.4 M) were tested in this work. The flux, recovery, and power density for