Is in this research review of the way minimum absolute deviations values ​​based on linear programming method to estimate the parameters of simple linear regression model and give an overview of this model. We were modeling method deviations of the absolute values ​​proposed using a scale of dispersion and composition of a simple linear regression model based on the proposed measure. Object of the work is to find the capabilities of not affected by abnormal values by using numerical method and at the lowest possible recurrence.

Statistical methods of forecasting have applied with the intention of constructing a model to predict the number of the old aged people in retirement homes in Iraq. They were based on the monthly data of old aged people in Baghdad and the governorates except for the Kurdistan region from 2016 to 2019. Using Box-Jenkins methodology, the stationarity of the series was examined. The appropriate model order was determined, the parameters were estimated, the significance was tested, adequacy of the model was checked, and then the best model of prediction was used. The best model for forecasting according to criteria of (Normalized BIC, MAPE, RMSE) is ARIMA (0, 1, 2).

The paper examines key aspects of the use of phraseologi-cal units related to colors in Russian culture and speech. It explores their role in shaping cultural identity, reflecting national characteristics and men-tality. The study analyzes the frequency and contexts of the use of color-related phraseological units in contemporary speech, as well as the influ-ence of media and literature on their popularization. The author highlights the significance of phraseological units in preserving cultural heritage and fostering a deeper understanding of language and culture.

Statistical methods of forecasting have applied with the intention of constructing a model to predict the number of the old aged people in retirement homes in Iraq. They were based on the monthly data of old aged people in Baghdad and the governorates except for the Kurdistan region from 2016 to 2019. Using BoxJenkins methodology, the stationarity of the series was examined. The appropriate model order was determined, the parameters were estimated, the significance was tested, adequacy of the model was checked, and then the best model of prediction was used. The best model for forecasting according to criteria of (Normalized BIC, MAPE, RMSE) is ARIMA (0, 1, 2)

This study provides valuable information on secondary microbial infections in H1N1 patients compared to Seasonal Influenza in Iraqi Patients. Nasopharynx  swabs were collected from  (12 ) patients  infected with Seasonal influenza (11  from Baghdad  and 1 Patient from south of Iraq) ,and ( 22 ) samples from patients with 2009 H1N1 ( 20 from Baghdad and  2 from  south of Iraq). The results show that the patients infected with 2009 H1N1 Virus were younger than healthy subjects and those infected with seasonal influenza. And the difference reached to the level of significance     (p< 0.01) compared with healthy subjects.Two cases infected with 2009 H1N1 virus (9.1%) were fro

The objective of this research is to know the economic feasibility of hydroponics technology by estimating the expected demand for green forage for the years 2021-2031 as well as Identify and analyze project data and information in a way that helps the investor make the appropriate investment decision in addition to preparing a detailed technical preliminary study for the cultivar barley project focusing on the commercial and financing aspects and the criteria that take into account the risks and uncertainties . that indicating the economic feasibility of the project to produce green forage using hydroponics technology. Cultured barley as a product falls within the blue ocean strategy. Accordingly, the research recommends the necess

Abstract

  Bivariate time series modeling and forecasting have become a promising field of applied studies in recent times. For this purpose, the Linear Autoregressive Moving Average with exogenous variable ARMAX model is the most widely used technique over the past few years in modeling and forecasting this type of data. The most important assumptions of this model are linearity and homogenous for random error variance of the appropriate model. In practice, these two assumptions are often violated, so the Generalized Autoregressive Conditional Heteroscedasticity (ARCH) and (GARCH) with exogenous varia