The measures taken by the state in previous years to reduce mortality and increase the birth rate have exhausted themselves: in recent years, in a number of regions, there has been an excess of mortality over birth rate (repetition of the "Russian cross"). In this regard, research is relevant, connected not only with identifying the effect of the Russian cross, but also determining the prospects for its repetition. Purpose: forecasting demographic processes in the region and identifying the causes of the demographic crisis. Methods: tabular and graphical methods for analyzing the ratio of fertility and mortality rates, correlation analysis to identify the relationship between the national composition of the region and the fertility rate, econometric methods for constructing and researching multidimensional time series, which consists in developing a vector model of error correction that allows studying mutual responses to shocks in dynamics and forecast the levels of fertility and mortality in the region. Results: the study made it possible to predict the most important demographic indicators in the region on the basis of a vector error correction model, which reflects both the short-term equilibrium between the dynamics of the birth rate and mortality rate, and allows us to correct the deviation from the long-term equilibrium based on taking into account the previous deviations from such an equilibrium. The forecasting results showed the persistence in the near future of imbalances in population reproduction, revealed the problem of divorce before the birth of the first child in families due to financial difficulties or fear of this, and confirmed the advisability of introducing new government measures aimed at increasing the birth rate and reducing mortality. Scientific novelty: the article for the first time uses the multidimensional time series toolkit in the form of a vector error correction model for predicting demographic processes in the Orenburg region. Practical significance: the proposed approach can be used in the analysis and forecasting of the effect of the "Russian cross" for any region of the Russian Federation, and the results obtained can be used by the authorities in the development of demographic and socio-economic programs to support the population.
Subject. The article considers the application of machine learning methods to analyze students' academic performance. Objectives. The aims are to identify factors influencing the academic performance of students, detect hidden patterns, useful and interpretable knowledge about the results of educational process and its participants, using the intellectual analysis of educational data. Methods. The study rests on methods of econometric modeling, multidimensional classification, and big data clustering. Results. The developed models of intellectual analysis of educational data enable to perform a comparative analysis of students and forecast the level of student’s mastering an educational program, depending on factors like the total score of entrance tests, average score of academic performance, basis and form of education, course, the level of training, student’s gender and age. Conclusions. The results of the application of machine learning methods to analyze academic performance will help differentiate effective teaching methods and technologies for groups of students with different levels of academic results, timely take corrective actions regarding students from risk group. Eventually, this will contribute to retention of students and improvement of the educational process quality.
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