Estimating the Multiple Regression Model Using the Bootstrap Method to Study the Effect of Environmental Wastes on the Waters of the Euphrates River

Abstract

The world's interest in the environment is due to the deterioration and continuous changes caused by the wrong human exploitation of it by throwing waste of different types, as the water environment is exposed daily to thousands of tons of different waste that led to its pollution, and these pollutants increase day after day, which leads to damage to the water environment, and Rivers are the primary source of natural life, and since the Euphrates River is one of the most important water sources and many life activities depend on it, it suffers from pollution issues resulting from household, industrial, and agricultural waste. Thus, samples from the river were investigated in relation to temperature to treat the river water at three different locations. The research is a practical procedure on the data to estimate the traditional multiple regression model using the bootstrap approach based on multiple comparison criteria. The research problem is based on the criterion of progress of countries not only in their economic, architectural, and military strength but also in the light of addressing climate change and environmental pollution that developed countries suffer from to provide health services. Its people are free from environmental pollution, and it is one of the main pillars in the development of Aldo. Therefore, protecting and improving water quality has become one of the important issues that all governments and environmental professionals work on to address and improve it. The goal of the study is to estimate and compare traditional multiple regression models using the bootstrap approach, and the results indicate that the initial station has a substantial effect on temperature. Through comparison using mean square error, to obtain the best model. The results showed that, compared to the standard multiple regression model, the bootstrap multiple regression model is more accurate and efficient.