The purpose behind building the linear regression model is to describe the real linear relation between any explanatory variable in the model and the dependent one, on the basis of the fact that the dependent variable is a linear function of the explanatory variables and one can use it for prediction and control. This purpose does not cometrue without getting significant, stable and reasonable estimatros for the parameters of the model, specifically regression-coefficients. The researcher found that "RUF" the criterian that he had suggested accurate and sufficient to accomplish that purpose when multicollinearity exists provided that the adequate model that satisfies the standard assumpitions of the error-term can be assigned. It is wrong to ignore the assumptions and depend directly on the least "MSE & PRESS" and greatest " " because it satisfies the model with false fit to data, whereas the regession coefficients are still unstable and unreasonable because of the multicollinearity and the effect of the error-term on the explanatory and predicted power. So the researcher has made procedures for using his criterion "RUF" to get the real best subset linear model.
In this paper, the deterministic and the stochastic models are proposed to study the interaction of the Coronavirus (COVID-19) with host cells inside the human body. In the deterministic model, the value of the basic reproduction number determines the persistence or extinction of the COVID-19. If , one infected cell will transmit the virus to less than one cell, as a result, the person carrying the Coronavirus will get rid of the disease .If the infected cell will be able to infect all cells that contain ACE receptors. The stochastic model proves that if are sufficiently large then maybe give us ultimate disease extinction although , and this facts also proved by computer simulation.
. In recent years, Bitcoin has become the most widely used blockchain platform in business and finance. The goal of this work is to find a viable prediction model that incorporates and perhaps improves on a combination of available models. Among the techniques utilized in this paper are exponential smoothing, ARIMA, artificial neural networks (ANNs) models, and prediction combination models. The study's most obvious discovery is that artificial intelligence models improve the results of compound prediction models. The second key discovery was that a strong combination forecasting model that responds to the multiple fluctuations that occur in the bitcoin time series and Error improvement should be used. Based on the results, the prediction a
... Show MoreIn recent years, Bitcoin has become the most widely used blockchain platform in business and finance. The goal of this work is to find a viable prediction model that incorporates and perhaps improves on a combination of available models. Among the techniques utilized in this paper are exponential smoothing, ARIMA, artificial neural networks (ANNs) models, and prediction combination models. The study's most obvious discovery is that artificial intelligence models improve the results of compound prediction models. The second key discovery was that a strong combination forecasting model that responds to the multiple fluctuations that occur in the bitcoin time series and Error improvement should be used. Based on the results, the prediction acc
... Show MoreInterval methods for verified integration of initial value problems (IVPs) for ODEs have been used for more than 40 years. For many classes of IVPs, these methods have the ability to compute guaranteed error bounds for the flow of an ODE, where traditional methods provide only approximations to a solution. Overestimation, however, is a potential drawback of verified methods. For some problems, the computed error bounds become overly pessimistic, or integration even breaks down. The dependency problem and the wrapping effect are particular sources of overestimations in interval computations. Berz (see [1]) and his co-workers have developed Taylor model methods, which extend interval arithmetic with symbolic computations. The latter is an ef
... Show MoreThis paper is specifically a detailed review of the Spatial Quantile Autoregressive (SARQR) model that refers to the incorporation of quantile regression models into spatial autoregressive models to facilitate an improved analysis of the characteristics of spatially dependent data. The relevance of SARQR is emphasized in most applications, including but not limited to the fields that might need the study of spatial variation and dependencies. In particular, it looks at literature dated from 1971 and 2024 and shows the extent to which SARQR had already been applied previously in other disciplines such as economics, real estate, environmental science, and epidemiology. Accordingly, evidence indicates SARQR has numerous benefits compar
... Show MoreIncludes search unemployment concept ... types, graduate unemployment a model introduction to the researcher tackled the problem of unemployment being dangerous to the community, it's also growing in size year after year is a waste of a clear human capabilities, also addressed the importance of the research being a touch on the problem of unemployment and its concept and try to find solutions to them , and then came the goals set by the search researcher identifies unemployment and their causes and consequences and to provide a true picture of the situation of unemployed graduates and disclosure about how they treat their graduates for jobs provide him with a decent life problem. And adopted a researcher on the use of a questionnaire add
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اهتم الفكر السياسي في القرنين الاخيرين بدراسة الطبقات على نحو غير مسبوق, واصبح موضوع التحليل الطبقي المعني بالطبقات من حيث تعريفها, وتحديد موقعها في السلم الاجتماعي, فضلاً عن نوعية العلاقة بين شرائحها وفئاتها المختلفة من حيث الصراع والتناغم, المادة الرئيسة والموضوع الاكثر اهمية في دراسات الفكر السياسي والاجتماعي.ومن بين الطبقات, احتلت الطبقة الوسطى مكا
... Show MoreThis paper is specifically a detailed review of the Spatial Quantile Autoregressive (SARQR) model that refers to the incorporation of quantile regression models into spatial autoregressive models to facilitate an improved analysis of the characteristics of spatially dependent data. The relevance of SARQR is emphasized in most applications, including but not limited to the fields that might need the study of spatial variation and dependencies. In particular, it looks at literature dated from 1971 and 2024 and shows the extent to which SARQR had already been applied previously in other disciplines such as economics, real estate, environmental science, and epidemiology. Accordingly, evidence indicates SARQR has numerous benefits compar
... Show MoreResearch indicates that the second half of the twentieth century marked large interests in the service industry by government and private organizations in that one, and the service industry has become the bedrock of plans in achieving economic and social development. From this standpoint felt specialists and researchers the importance of transport modes, including rail, which should be available between Almnltq Civil populated as services organized by the competent authorities to achieve the active participation of citizens in economic and social development in the region and that the term services means economic activities, which are the results Pollack concrete such as accepting the situation and satisfaction them or satisfacti
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