Chemical pollution is a very important issue that people suffer from and it often affects the nature of health of society and the future of the health of future generations. Consequently, it must be considered in order to discover suitable models and find descriptions to predict the performance of it in the forthcoming years. Chemical pollution data in Iraq take a great scope and manifold sources and kinds, which brands it as Big Data that need to be studied using novel statistical methods. The research object on using Proposed Nonparametric Procedure NP Method to develop an (OCMT) test procedure to estimate parameters of linear regression model with large size of data (Big Data) which comprises many indicators associated with chemi

In this article, a new deterministic primality test for Mersenne primes is presented. It also includes a comparative study between well-known primality tests in order to identify the best test. Moreover, new modifications are suggested in order to eliminate pseudoprimes. The study covers random primes such as Mersenne primes and Proth primes. Finally, these tests are arranged from the best to the worst according to strength, speed, and effectiveness based on the results obtained through programs prepared and operated by Mathematica, and the results are presented through tables and graphs.

A simplified theoretical comparison of the hydrogen chloride (HCl) and hydrogen fluoride (HF) chemical lasers is presented by using computer program. The program is able to predict quantitative variations of the laser characteristics as a function of rotational and vibrational quantum number. Lasing is assumed to occur in a Fabry-Perot cavity on vibration-rotation transitions between two vibrational levels of hypothetical diatomic molecule. This study include a comprehensive parametric analysis that indicates that the large rotational constant of HF laser in comparison with HCl laser makes it relatively easy to satisfy the partial inversion criterion. The results of this computer program proved their credibility when compared with th

Generally, statistical methods are used in various fields of science, especially in the research field, in which Statistical analysis is carried out by adopting several techniques, according to the nature of the study and its objectives. One of these techniques is building statistical models, which is done through regression models. This technique is considered one of the most important statistical methods for studying the relationship between a dependent variable, also called (the response variable) and the other variables, called covariate variables. This research describes the estimation of the partial linear regression model, as well as the estimation of the “missing at random” values (MAR). Regarding the

Theresearch took the spatial autoregressive model: SAR and spatial error model: SEM in an attempt to provide a practical evident that proves the importance of spatial analysis, with a particular focus on the importance of using regression models spatial andthat includes all of them spatial dependence, which we can test its presence or not by using Moran test. While ignoring this dependency may lead to the loss of important information about the phenomenon under research is reflected in the end on the strength of the statistical estimation power, as these models are the link between the usual regression models with time-series models. Spatial analysis had

The estimation of the parameters of linear regression is based on the usual Least Square method, as this method is based on the estimation of several basic assumptions. Therefore, the accuracy of estimating the parameters of the model depends on the validity of these hypotheses. The most successful technique was the robust estimation method which is minimizing maximum likelihood estimator (MM-estimator) that proved its efficiency in this purpose. However, the use of the model becomes unrealistic and one of these assumptions is the uniformity of the variance and the normal distribution of the error. These assumptions are not achievable in the case of studying a specific problem that may include complex data of more than one model. To

The research took the spatial autoregressive model: SAR and spatial error model: SEM  in an attempt to provide practical evidence that proves the importance of spatial analysis, with a particular focus on the importance of using regression models spatial and that includes all of the spatial dependence, which we can test its presence or not by using Moran test. While ignoring this dependency may lead to the loss of important information about the phenomenon under research is reflected in the end on the strength of the statistical estimation power, as these models are the link between the usual regression models with time-series models. The spatial analysis had been applied to Iraq Household Socio-Economic Survey: IHS

In this paper, we investigate the automatic recognition of emotion in text. We perform experiments with a new method of classification based on the PPM character-based text compression scheme. These experiments involve both coarse-grained classification (whether a text is emotional or not) and also fine-grained classification such as recognising Ekman’s six basic emotions (Anger, Disgust, Fear, Happiness, Sadness, Surprise). Experimental results with three datasets show that the new method significantly outperforms the traditional word-based text classification methods. The results show that the PPM compression based classification method is able to distinguish between emotional and nonemotional text with high accuracy, between texts invo