Module code and title: Dissertation-6econ012C-n

Methodology
This research consisted primarily of an experimental nature. To analyze the recordings, empirical methodologies were employed in collaboration, and appropriate statistical tests were conducted to test the hypothesis. This empirical study used an OLS simple regression model with panel data to analyze the connection and share found by the different factors in order to make statistical inferences about whether education has a positive or negative impact on labor market balance, decreasing poverty levels, welfare, and fixedness, property, and developing urban and rural areas of population. The investigation's main goals are as follows:

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  Analyzing Asian countries education system in recent years
  

The focus of this study was mainly on experimentation and obtained empirical date paper within a decade panel research had been used from Edu.uz, Gov.uz and OECD countries, IMF and World Bank database during 2012 and 2022. This research we try analyze more than 8 Asian countries. Actual approaches were used in conjunction to evaluate the data, and suitable statistical analyses were run to confirm the assumption. An econometric model GMM was used to determine if there was a relationship between education and poverty alleviation of government. Incidence of poverty was used as a regression variables (Y), whereas educational attainment, political and social involvement, interest rate, growing population, GDP growth, gross national loans and Gain ratio were being used as independent factors in the model. Even so, the information gathered should be categorized. In the other phrases, not all factors are expressed in percentage values, a few were expressed in index forms, others in unit form, and the others were expressed in terms of percentage. Skewness and correlation concerns evaluated factors ought to be identical values to prevent any errors. The logarithmic method in STATA software allows you to convert values into percentage. The above empirical analysis used an OLS simple regression analysis with data set to examine the relationship and start sharing observed by the numerous variables in addition to making statistical conclusions of whether education does have a substantial effect balance on labor market, reducing levels of poverty, property, fixedness, population and welfare development in rural and urban areas. In addition, when researched to analyze Uzbekistan data or information we have meet some difficulties. Because it is too difficult to find information about our country and other central Asia countries. In the net lack of reliable source and information these counties took long time. The random effect model, also known as such component variance model in statistics, is indeed a statistical method in which the variables are statistically independent. It is a classified linear model that was created by rating several populations. If there are no inelastic factors, multiple regression models are utilized in categorized or panel analyses of data set in econometrics. Random effect methods are useful for dealing with non-uniformity in which heterogeneity persists during time and is unrelated to independent factors. Because choosing the initial variance eliminates the desired time - unpredictable elements of the model - the above consistency can be avoided by removing it from the data collected. In this study, 8 Asian nations were chosen for sample in order to see how impact of education affect decreasing poverty level. The following is a basic technique to model the relationships between these statistics: