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Документ Application of Multidimensional Statistical Analysis Technology for Grouping Regions by the Investment Attractiveness Level(SCITEPRESS – Science and Technology Publications, Lda , Setúbal, Portugal, 2023) Hryhoruk, Pavlo; Khrushch, Nila; Grygoruk, Svitlana; Ovchynnikova, OlenaThe paper is devoted to studying multidimensional statistical analysis tools for grouping regions by the level of their investment attractiveness and identifying changes in the structure of regions in the context of the continued destructive impact of the COVID-19 pandemic. An analysis of approaches to assessing investment attractiveness identified their strengths. Insufficient attention to the application of methods of multidimensional statistical analysis to a grouping of regions is stated. The authors consider the clustering of regions of Ukraine in the context of their level of investment attractiveness by the method of k-means and identify their structure according to the level of investment attractiveness in 2019 and 2020 in the context of the COVID-19 pandemic. To verify the correctness of the conclusions, the method of principal components with the rotation of the space of the selected factors by the quartimax technique. Further grouping of regions in the space of selected principal components showed results identical to the application of the cluster analysis method. Potential investors can use the research results to determine priority areas of investment. Also, the results are useful for local self-government bodies, as they provide information on the relative level of investment attractiveness of a specific region compared to other territorial units and also allow identifying weak points in specific areas of activityДокумент Modeling of investment processes by methods of regression analysis(Khmelnytskyi National University, 2019) Ovchynnikova, Olena; Zavhorodnia, TetianaThe article presents developed models of the influence of indicators of economic, separately construction and tourism spheres on the total amount of capital investments in Khmelnytsky region. For each group of indicators, from 5 to 9 single- and multi-factor models that reflect the degree of influence of certain variables on capital investments in the region. The developed models made it possible to conclude that the investment sphere depends on the economic, construction and tourism indicators of the region. Some models have shown a low level of correlation, other built models of investment processes are qualitative, adequate and reflect the real impact of the selected factors on the resulting indicator and can be used for further forecasting.Документ Multidimensional statistical analysis of investment attractiveness and regional changes in the COVID-19 pandemic(2023) Hryhoruk, Pavlo; Khrushch, Nila; Grygoruk, Svitlana; Ovchynnikova, OlenaThis paper delves into an in-depth exploration of multidimensional statistical analysis techniques aimed at categorizing regions based on their levels of investment attractiveness, while also scrutinizing the evolving regional structures in light of the persistent and adverse effects of the COVID-19 pandemic. Through a comprehensive review of various approaches to assessing investment attractiveness, the study highlights their respective strengths. Notably, the research underscores the underutilization of multidimensional statistical analysis methodologies in the regional grouping context. The authors undertake the task of clustering Ukrainian regions based on their investment attractiveness levels, employing the well-regarded 𝑘-means method. This analysis extends to the identification of the regions’ investment attractiveness structure in both 2019 and 2020, amid the COVID-19 pandemic. Substantiating the validity of their findings, the authors employ the principal component method in conjunction with the quartimax technique to rotate the space of selected factors. Remarkably, the subsequent regional grouping in this transformed principal component space mirrors the outcomes of the cluster analysis method. The research outcomes hold practical value for potential investors, enabling them to pinpoint key investment areas. Furthermore, local self-governing bodies stand to benefit from these findings, gaining insights into specific regions’ relative investment attractiveness levels compared to their counterparts, while also uncovering vulnerabilities in distinct activity domainsДокумент Post-COVID-19 economic recovery in the context of SDG8 and SDG9: the case of selected Eastern European countries(2023) Hryhoruk, Pavlo; Khrushch, Nila; Grygoruk, Svitlana; Ovchynnikova, OlenaAssessing the economic development of countries in the context of the tasks defined in the UN 2030 Agenda for Sustainable Development is essential from the point of view of determining progress in achieving the SDGs. It becomes especially relevant in periods of global challenges and disturbances, one of which is the COVID-19 pandemic. The goals of SDG8 and SDG9 contain indicators that are determined mainly by the state of development of the country’s economy, so the analysis of trends in their changes is important in the context of identifying trends in economic growth in general, as well as for evaluating progress in achieving these goals. The purpose of the study is to identify the impact of the COVID-19 pandemic on the economic development of countries to achieve sustainable development goals and assess progress trends in the post-pandemic recovery of the economies of Eastern Europe by analyzing quantitative data from official statistical sources. The object of the study is the economic development of Eastern European countries in the conditions of the COVID-19 pandemic. The study period covers the time range from 2017 to 2021 and includes both the pre-pandemic and pandemic periods. Eight countries of Eastern Europe were chosen as research objects. Indicators of official statistics related to SDG8 and SDG9 sub-goals were selected for analysis. The study showed a significant decrease in the values of most indicators, which was caused by the destructive effect of the pandemic. To assess the possible development trajectory, we calculated the estimated value of indicators for 2022 using the Holt-Winters method. The results showed that, despite some progress in 2021, in 2022, the values of the indicators are decreasing. Such estimates correspond to the trends provided by international institutions. We have built a composite indicator to assess the economic development trend comprehensively. The results of the evaluation confirmed the general trend towards a decrease in the level of economic growth in the context of the goals of SDG8 and SDG9 for all the countries of Eastern Europe selected in the studyДокумент Predicting the number of public projects in the region as an indicator of economic stability(2019) Dupliak, Olha; Ovchynnikova, Olena; Zamazii, OksanaThe article examines the issues of submitting applications and receiving funding for public projects depending on the demographic situation in the region. It is shown that the activity of the population in submitting applications for community projects contributes to the effectiveness of investing in the development of cities and regions. The dynamics of project submission in connection with the permanent population of Khmelnytskyi region is analyzed. In the course of the analysis, it was found that women make up the largest share among both the population and those who submit social projects. According to age characteristics, in spite of an increase in the proportion of the older age groups, mainly younger and middle age groups of the population develop community-based projects. The gender trend identified during the study indicates that women are more active in local processes than men who more closely coordinate these projects with government agencies. According to these statistics, a forecast is provided as to allocation of funds for the public projects’ financing.