Object: The object of the current article is to build a linear regression model to predict the value of GDP per capita using 9 socio-economic indicators. Methods: Correlation and regression analysis methods were used for this study. All calculations are performed in MS EXCEL. 1 dependent and 9 independent variables were used as a basis. Findings: Authors have identified nine variables that can potentially affect the amount of GDP per capita, and col lected data according to these variables from 2012 to 2022, from open sources. The model was highly appreciated by generally accepted estimates, including: f value, p value, normalized R square. Conclusions: Our analysis shows the impact of average wages, human development index, fertility and degree of real interest on GDP per capita, which allows us to build a highly efficient linear regression model. Thanks to our abil ity to accurately assess and reliably predict, our results shed light on the directions of economic intervention aimed at economic growth and improving the welfare and development of society in Kazakhstan.
Berdimurat et al. (Sat,) studied this question.